HIGH-DIMENSIONAL ESTIMATION WITH GEOMETRIC
CONSTRAINTS

arXiv:1404.3749v2 [math.PR] 19 May 2016

YANIV PLAN, ROMAN VERSHYNIN, AND ELENA YUDOVINA
Abstract. Consider measuring a vector x ∈ Rn through the inner product with
several measurement vectors, a1 , a2 , . . . , am . It is common in both signal processing and statistics to assume the linear response model yi = hai , xi + εi , where εi
is a noise term. However, in practice the precise relationship between the signal
x and the observations yi may not follow the linear model, and in some cases it
may not even be known. To address this challenge, in this paper we propose a
general model where it is only assumed that each observation yi may depend on
ai only through hai , xi. We do not assume that the dependence is known. This is
a form of the semiparametric-single index model, and it includes the linear model
as well as many forms of the generalized linear model as special cases. We further
assume that the signal x has some structure, and we formulate this as a general
assumption that x belongs to some known (but arbitrary) feasible set K ⊆ Rn .
We carefully detail the benefit of using the signal structure to improve estimation.
The theory is based on the mean width of K, a geometric parameter which can
be used to understand its effective dimension in estimation problems. We determine a simple, efficient two-step procedure for estimating the signal based on this
model—a linear estimation followed by metric projection onto K. We give general conditions under which the estimator is minimax optimal up to a constant.
This leads to the intriguing conclusion that in the high noise regime, an unknown
non-linearity in the observations does not significantly reduce one’s ability to
determine the signal, even when the non-linearity may be non-invertible. Our results may be specialized to understand the effect of non-linearities in compressed
sensing.

1. Introduction
An important challenge in the analysis of high-dimensional data is to combine
noisy observations—which individually give uncertain information—to give a precise
estimate of a signal. One key to this endeavor is to utilize some form of structure
of the signal. If this structure comes in a low-dimensional form, and the structure
is aptly used, the resulting dimension reduction can find a needle of signal in a
haystack of noise. This idea is behind many of the modern results in compressed
sensing [19].
Date: April 14, 2014.
2010 Mathematics Subject Classification. 60D05 (primary), 94A12 (secondary).
Key words and phrases. High-dimensional inference, semiparametric single-index model, compressed sensing, matrix completion, mean width, dimension reduction.
Y.P. was partially supported by an NSF Postdoctoral Research Fellowship under award No.
1103909. R.V. was partially supported by NSF grants DMS 1161372, 1001829, 1265782 and USAF
Grant FA9550-14-1-0009. E.Y. was partially supported by an NSF Postdoctoral Research Fellowship
under award No. 1204311.
1

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YANIV PLAN, ROMAN VERSHYNIN, AND ELENA YUDOVINA

To make this more concrete, suppose one is given a series of measurement vectors
ai , paired with observations yi . It is common to assume a linear data model yi =
hai , xi + εi , relating the response to an unknown signal, or parameter vector, x.
However, in many real problems the linear model may not be justifiable, or even
plausible—consider binary observations. Such data can be approached with the
semiparametric single index model, in which one models the data as
yi = f (hai , xi + εi )
for some unknown function f : R → R. The goal is to estimate the signal x despite
the unknown non-linearity f . In fact, one may take a significantly more general
approach as described in the next section.
It is often the case that some prior information is available on the structure of the
signal x. Structure may come in many forms; special cases of interest include manifold structure, sparsity, low-rank matrices and compressible images. We consider a
general model, assuming only that
x ∈ K for some closed star-shaped1set K ⊂ Rn .
This includes cones and convex sets containing the origin. We focus on three goals
in this paper:
1) determine general conditions under which the signal can be well estimated;
2) give an efficient method of estimation;
3) determine precisely what is gained by utilizing the feasible set K that encodes
signal structure.
Let us outline the structure of this paper. In the rest of this section, we carefully
specify our model, describe the proposed estimator, and analyze its performance
for general K; the main result is Theorem 1.3 in Section 1.5. In Section 2, we
specialize our results to a number of standard feasible sets K, including sparse
vectors and low-rank matrices. In Section 3, we specialize our results to specific
versions of the semiparametric single-index model. We include the linear model, the
binary regression model, and a model with explicit non-linearity. In Section 4, we
discuss the optimality of the estimator. We show that it is minimax optimal up to a
constant under fairly general conditions. In Section 5.1 we illustrate the connection
between our results and a deep classical estimate from geometric functional analysis,
called the low M ∗ estimate. This estimate tightly controls the diameter of a random
section of an arbitrary set K. Section 6 gives an overview of the literature on the
semiparametric single-index model, emphasizing the differences between the more
geometric approach in this paper and the classical approaches used in statistics
and econometrics. We give some concluding remarks in Section 7. Sections 8–10
contain the technical proofs. In Section 8 we give the proofs of the results from
Section 1, including our main result; in Section 9 we state and prove a version of
our main result which holds with high probability rather than in expectation; and
in Section 10 we give proofs of the optimality results from Section 4.
Throughout this paper, we use notation common in the compressed sensing literature. However, because we expect our results to be of interest to a wider statistical
audience, we provide a dictionary of notation in Section 6, Table 1.
1A set K is called star-shaped if λK ⊆ K whenever 0 ≤ λ ≤ 1.

HIGH-DIMENSIONAL ESTIMATION WITH GEOMETRIC CONSTRAINTS

3

1.1. Model. Let x ∈ Rn be a fixed (unknown) signal vector, and let ai ∈ Rn be
independent random measurement vectors. We assume that observations yi are
independent real-valued random variables such that
each observation yi may depend on ai only through hai , xi.

(1.1)

In other words, we postulate that, given hai , xi, the observation yi and the measurement vector ai are conditionally independent. We are interested in recovering the
signal vector x using as few observations as possible. Furthermore, we will usually
assume some a priori knowledge about x of the form x ∈ K for some known set
K ⊂ Rn . Note that the norm of x is sacrificed in this model since it may be absorbed
into the dependence of yi on hai , xi. Thus, it is of interest to estimate x up to a
scaling factor.
Unless otherwise specified, we assume that ai are independent standard normal
vectors in Rn . We lift this assumption in several places in the paper: see the matrix
completion problem in Section 2.5, and the lower bounds on all possible estimators in
Section 4. We make further suggestions of how this assumption may be generalized
in Section 7.
1.2. Linear estimation. The first and simplest approach to estimation is to ignore
the feasible set K for a moment. One may then employ the following linear estimator
m

x
blin

1 X
y i ai .
:=
m
i=1

It is not difficult to see that x
blin is an unbiased estimator of the properly scaled vector
x, and to compute the mean squared error. This is the content of the following
proposition whose proof we defer to Section 8.
Proposition 1.1 (Linear estimation). Let x̄ = x/kxk2 . Then
i
1h 2
σ + η 2 (n − 1) .
Ex
blin = µx̄ and E kb
xlin − µx̄k22 =
m
Here

µ = E y1 ha1 , x̄i ,

σ 2 = Var(y1 ha1 , x̄i),

η 2 = E y12 .

(1.2)

By rotation invariance of ai , the parameters µ, σ and η depend on the magnitude
kxk2 but not on the direction x̄ = x/kxk2 of the unknown vector x or on the
number of observations m. These properties make it simple to compute or bound
these parameters in many important cases, as will be clear from several examples
below. For now, it is useful to think of these parameters as constants.
The second part of Proposition 1.1 essentially states that
i1/2
h
√ 
1 
≍ √ σ+η n .
E kb
xlin − µx̄k22
m

(1.3)

We can express this informally as follows:

Linear estimation is accurate for m = O(n) observations.

(1.4)

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YANIV PLAN, ROMAN VERSHYNIN, AND ELENA YUDOVINA

1.3. Projection onto the feasible set. Although estimate (1.3) on the accuracy
of linear estimation is sharp, it can be significantly improved if some prior information is available about the vector x. A rigorous way to encode the prior information
would be to assume that x ∈ K where K ⊂ Rn is some fixed, closed, and known
feasible set. Because the scaling of x may be absorbed into the semiparametric
single-index model, it is actually more natural to assume that
µx̄ ∈ K

(1.5)

x
b = PK (b
xlin ) = arg min kb
xlin − zk2 .

(1.6)

in the notation of Proposition 1.1.2
Recall that x
blin is an unbiased estimator of µx̄, a vector that lies in K. So to
incorporate the feasible set K into estimation, a natural step is to metrically project
the linear estimator x
blin onto K. In other words, we define
z∈K

As we will see shortly, this non-linear estimator outperforms the linear one, often
by a big margin.
1.4. Measuring the size of the feasible set by mean width. The quality of
the non-linear estimator (1.6) should depend on the size of the feasible set K. It
turns out that there is a simple geometric notion that captures the size of K for this
purpose. This notion is the local mean width. At first reading, one may replace it
with a slightly simpler concept of global mean width.
Definition 1.2 (Mean width). The (global, Gaussian) mean width of a subset K ⊂
Rn is defined as
w(K) = E sup hg, x − yi ,
x,y∈K

where g ∼ N (0, In ). The local mean width of a subset K ⊂ Rn is a function of scale
t ≥ 0, and is defined as
wt (K) = E

sup
x,y∈K, kx−yk2 ≤t

hg, x − yi .

Note that wt (K) ≤ w(K) trivially holds for all t. The concepts of mean width,
both global and local, originate in geometric functional analysis and asymptotic
convex geometry, see e.g. [23]. Quantities equivalent to mean width appear also
in stochastic processes under the name of γ2 functional (see [53]), and in statistical
learning theory under the name of Gaussian complexity (see [5]).
More recently, the role of mean width (both local and global) was recognized in
the area of signal recovery [37, 45, 43, 3, 1]. To interpret these developments as well
as this paper, it is often helpful to think of the square of the mean width of the
properly scaled feasible set K as an essential dimension (as opposed to the algebraic
dimension, which typically equals n). A notable benefit of this method of measuring
dimension is that it is robust to perturbations: If K is slightly increased, the mean
width only changes slightly.
2Passing between assumptions x ∈ K and µx̄ ∈ K in practice should be painless by rescaling
K, as the scaling factor µ is usually easy to estimate. Further, if K is a cone, then there is no
difference between the assumptions.

HIGH-DIMENSIONAL ESTIMATION WITH GEOMETRIC CONSTRAINTS

5

1.5. Main result.
Theorem 1.3 (Non-linear estimation). Let x̄ = x/kxk2 . Assume that µx̄ ∈ K
where K is a fixed star-shaped closed subset of Rn . Then the non-linear estimator
x
b defined in (1.6) satisfies the following for every t > 0:
wt (K) i
2 h
.
(1.7)
E kb
x − µx̄k2 ≤ t + √ σ + η
t
m
Here µ, σ and η are the numbers defined in (1.2).

In Section 4, we will give general conditions under which the error achieved in
this theorem is minimax optimal up to a constant factor—thus the theorem gives a
precise, non-asymptotic, characterization of the benefit of using the signal structure
K to improve the estimation. In Section 9 we will state a version of Theorem 1.3
whose conclusion is valid with high probability rather than in expectation. In the
next two sections we will simplify Theorem 1.3 in special cases, show how it is
superior to Proposition 1.1, and illustrate it with a number of examples.
2. Feasible sets K: consequences and examples
In this section we state a simpler version of Theorem 1.3, compare Theorem 1.3
with Proposition 1.1, and illustrate it with several classes of feasible sets K that
may be of interest in applications.
2.1. General sets: error bounds via global mean width. Let us state a simpler
conclusion of Theorem 1.3, in terms of global mean width w(K) and without the
parameter t.
Let K be an arbitrary compact star-shaped subset of Rn . Replacing in (1.7) the
local mean width wt (K) by the bigger quantity w(K) and optimizing in t, we obtain
√ h η w(K) i1/2
2σ
.
(2.1)
E kb
x − µx̄k2 ≤ √ + 2 2 √
m
m

While this conclusion is less precise than (1.7), it may be sufficient in some applications. We note the unusual rate O(m−1/4 ) in the right hand side, which nevertheless
can be sharp for some signal structures such as the ℓ1 -ball. See Sections 2.6, 4.3 and
also [48].
2.2. General sets: comparison with linear estimation. Let us compare the
qualities of the linear and non-linear estimators. Let K be an arbitrary star-shaped
closed subset of Rn . The definition of local mean width implies that
√
(2.2)
wt (K) ≤ w(tB2n ) = t w(B2n ) = t E kgk2 ≤ t(E kgk22 )1/2 = t n.

(Here and below, B2n is the n-dimensional Euclidean ball.) Substituting this bound
into (1.7) and letting t → 0, we deduce the following estimate from Theorem 1.3:
√ 
2 
E kb
x − µx̄k2 ≤ √ σ + η n .
m
Note that this is the same upper bound as Proposition 1.1 gives for linear estimation,
up to an absolute constant factor. We can express this conclusion informally as
follows:

6

YANIV PLAN, ROMAN VERSHYNIN, AND ELENA YUDOVINA

Projecting the linear estimator x
blin onto the feasible set K can only
improve the accuracy of estimation.
We will shortly see that such improvement is often significant.
2.3. General cones. Let K be a fixed closed cone in Rn , so tK = K is satisfied
for all t ≥ 0. Then
wt (K) = t w1 (K).
Substituting this into (1.7) and letting t → 0, we can state Theorem 1.3 in this case
as follows.
Theorem 2.1 (Estimation in a cone). Assume that x ∈ K where K is a fixed closed
cone in Rn . Let x̄ = x/kxk2 . Then

2 
(2.3)
E kb
x − µx̄k2 ≤ √ σ + η w1 (K) .
m
Here µ, σ and η are the numbers defined in (1.2).
To further simplify the bound (2.3), note that term containing w1 (K) essentially
dominates there. This is based on the following observation.
p
Lemma 2.2. For a nonempty cone K, one has E w1 (K) ≥ 2/π.

Proof. Note that K −K contains a line, so (K −K)∩B2n contains a pair of antipodal
points on the unit sphere. Therefore w1 (K) = w((K − K) ∩ B2n ) is bounded below
by
p the first absolute moment of the standard normal distribution, which equals
2/π.

Using Lemma 2.2, we see that (2.3) implies that
w1 (K)
,
E kb
x − µx̄k2 ≤ γ √
m

where γ =

√

2π σ + 2η.

(2.4)

In particular, we arrive at the following informal conclusion:
Linear estimation followed by projection onto the feasible cone K is
accurate for m = O(w1 (K)2 ) observations.
Remark 2.3 (Essential dimension). This result becomes especially transparent if we
think of w1 (K)2 as the essential dimension of the cone K. A good estimation is then
guaranteed for the number of observations proportional to the essential dimension of
K. This paradigm manifested itself in a number of recent results in the area of signal
recovery [37, 45, 43, 3, 1]. Among these we especially note [3] where the results are
explicitly stated in terms of a “statistical dimension”, a very close relative of w1 (K).
Remark 2.4 (Projecting onto S n−1 ). Since magnitude information about x is irrecoverable in the semiparametric model, and K has a conic, scale-invariant structure in
this subsection, it is natural to rephrase our theorem in terms of the estimation of
x̄, unscaled. One may then normalize x
b to come to the following conclusion:
2γ w1 (K)
x
b
− x̄ ≤
· √
.
(2.5)
kb
xk 2
µ
m
2

Indeed, this follows because
x
b
x
b
x
b
− x̄ ≤
−
kb
xk2
kb
xk 2 µ
2

+
2

x
b
− x̄
µ

2

≤2

x
b
− x̄
µ

2

HIGH-DIMENSIONAL ESTIMATION WITH GEOMETRIC CONSTRAINTS

7

where the last step follows since x
b/ kb
xk2 is the closest point in S n−1 to x
b/µ.

A quick computation similar to (2.2) shows that the essential dimension of a
cone is always bounded by its algebraic dimension. Thus the non-linear estimator
x
b outperforms the linear estimator x
blin discussed in (1.4), and the improvement is
2
dramatic in cases where w1 (K) ≪ n. We give examples of such situations below.

2.4. The set of sparse vectors. Sparsity is a key signal structure used in many
modern applications. For example, it is common to assume that only a small subset
of coefficients are significant in a regression model. As another example, images
are generally compressible in some dictionary, i.e., up to a linear transformation
an image is sparse. The compressed sensing model (see the book [19]) is based
on sparsity, and one may use the results in this paper as a treatment of unknown
non-linearities in compressed sensing.
The sparsity model takes K = {x ∈ Rn : |supp(x)| ≤ s}, i.e., K is the set of
vectors with at most s nonzero entries. Fortunately, projecting onto K is computationally efficient: one only needs to retain the s largest entries of the vector,
replacing all other entries by 0. This is referred to as hard thresholding. Thus, the
projection estimator is quite amenable to large data.
Further, when s is significantly smaller than n, projecting onto K gives a fruitful
dimension reduction. This is evident from the calculation
p
p
c s log(2n/s) ≤ w1 (K) ≤ C s log(2n/s)
which is given in [45, Lemma 2.3]. Thus, m ∼ s log(2n/s) observations are sufficient
to estimate an s-sparse vector in Rn .
Sparse in a dictionary. In many applications, the signal may be sparse in a
dictionary, rather than canonically sparse, e.g., images are often compressible in a
wavelet basis. In this case, the feasible set K ⊆ Rq is defined as
K = {Dv : |supp(v)| ≤ s}

where the matrix D ∈ Rq×n is referred to as a dictionary. A computation of the
essential dimension of K can be made through a slight generalization of [45, Lemma
2.3] (via a covering argument), which shows that once again
p
w1 (K) ≤ C s log(2n/s).

While projection onto K can pose a challenge, we will consider signals which are
approximately sparse in a dictionary in Section 2.6; in this case projection can be
done efficiently through convex programming.
2.5. The set of low-rank matrices. The low-rank signal structure is prevalent in
statistical applications, thus leading to the popularity of principal component analysis; for many examples, see [30]. Reconstructing a low-rank matrix from linear
combinations of entries arises in various applications such as quantum state tomography or recommender systems [49, 26]. Recently, there has been quite a bit of
interest and new theory on the problem of matrix completion: reconstruction of a
low-rank matrix from a subset of its entries [11, 10], with binary non-linearities given
special consideration [18, 51]. In this subsection, we first specialize our theory to
the low-rank structure and then adjust our theory to the matrix completion model.

8

YANIV PLAN, ROMAN VERSHYNIN, AND ELENA YUDOVINA

Here we take the cone K to be the set of d1 × d2 matrices with rank bounded by
some small r. Projection onto K requires only taking the singular value decomposition and keeping the largest r singular values and singular vectors. This procedure
is referred to as hard thresholding of singular values. The mean width of K is easy
to estimate (see e.g. [45]); one has
p
w1 (K) ≤ 2r(d1 + d2 ).
Thus, m ∼ r(d1 + d2 ) observations are sufficient to estimate a d1 × d2 matrix of rank
r.

We now move away from the assumption of Gaussian measurement vectors in
order to accommodate the matrix completion model. In this case, one observes a
random sample of the entries of the matrix X (linear observations); that is, the
measurement “vectors” (matrices) ai are uniformly distributed on the set of matrices with exactly one nonzero entry, and we take yi = hai , Xi. Projection-based
estimators have been considered for matrix completion in [31, 32, 12]. These papers
take general models, which require a somewhat more complicated theory. We consider a simple projection-based estimator and prove that it is quite accurate under
a simple model, recovering best-known theory for this model. The proof takes much
in common with known matrix completion theory; the purpose of putting it here is
to emphasize its shortness under a simple model. Further, this gives an intuition as
to how to extend our results in general to non-Gaussian measurements.
Let us consider the following simple matrix completion model. Take d1 = d2 = d
and consider a random subset Ω ⊂ {1, . . . , d} × {1, . . . , d}, which includes each entry
(i, j) with probability p, independently of all other entries. Let the observations yi
give the entries of the matrix X contained in Ω. Further, assume that X satisfies
the following incoherence condition: each entry of X is bounded by a parameter ζ.
Let us specify the estimator of X. Define the mask matrix ∆Ω with 0, 1 entries
by
(∆Ω )ij = 1{(i,j)∈Ω} .
1 Pm
Then the linear estimator discussed in Section 1.2, m
i=1 yi ai , is naturally replaced
1
with the Hadamard (entry-wise) product p ∆Ω ◦X. Clearly, this is a rough estimator
of X, but nevertheless it is an unbiased estimator. Fortunately, the projection step
can significantly improve the rough estimator. As above, project p1 ∆Ω ◦ X onto the
b
set of rank-r matrices, and call this X.
Under this model, we prove that the estimator is accurate, thereby recovering
best-known theory for this model.
Proposition 2.5 (Matrix completion accuracy). Consider the model described above.
b has the folLet m := E |Ω| = pd2 , and assume that m ≥ d log d. The estimator X
lowing average error per entry:
r
1
b − XkF ≤ C rd ζ.
E kX
d
m

Remark 2.6 (The benefit of projection). Despite the non-Gaussian form
pof the measurements,
the
estimation
error
in
matrix
completion
is
proportional
to
w1 (K)/m =
p
rd/m.

HIGH-DIMENSIONAL ESTIMATION WITH GEOMETRIC CONSTRAINTS

9

Remark 2.7. As is evident from the proof below, if the observations are corrupted
with i.i.d. N (0, ν 2 ) noise, the error bound becomes
r
1
b − XkF ≤ C rd (ζ + ν).
E kX
d
m

When ν ≥ ζ, this error is known to be minimax optimal up to a constant.
The noise is easily incorporated and carried through the proof. At the end it is
simply necessary to replace the scaled Rademacher matrix ζ · R by the sum of ζ · R
and a noise matrix.
b − X) ≤ 2r. It follows that
Proof of Proposition 2.5. Note that rank(X
√
b − Xk.
b − XkF ≤ 2rkX
kX
The operator norm may be bounded as follows:

b − Xk ≤ kX
b − p−1 ∆Ω ◦ Xk + kp−1 ∆Ω ◦ X − Xk ≤ 2p−1 k∆Ω ◦ X − pXk,
kX

b is the closest rank-r matrix to p−1 ∆Ω ◦ X in
where the last step follows since X
operator norm. The right-hand side is the operator norm of a matrix with independent, mean-zero entries and may be controlled directly in many ways. To give an
optimal result without log factors, one may use techniques from empirical process
theory and random matrix theory. By symmetrization [35, Lemma 6.3] followed by
the contraction principle [35, Theorem 4.4],
E k∆Ω ◦ X − pXk ≤ 2 E kR ◦ ∆Ω ◦ Xk ≤ 2ζ E kR ◦ ∆Ω k
where R is a matrix whose entries are independent Rademacher random variables
(i.e. Rij = 1 or −1 with probability 1/2). Thus, R ◦ ∆Ω has independent, identically
distributed entries. Seginer’s theorem [50] then gives
p
E kR ◦ ∆Ω k ≤ C pd

where C is a numerical constant. For the above inequality to hold, we must have
p ≥ log(d)/d, i.e., on average at least log(d) observations per row. Putting all of
this together gives
s
b − XkF ≤ C rd ζ.
E kX
p
Divide through by d and recall m = pd2 to conclude.



2.6. The set of approximately sparse vectors: ℓ1 -ball. In real applications,
exact sparsity is unusual, and is usually replaced with approximate sparsity. A clean
way to do this is to assume that the signal belongs to a scaled ℓp ball in Rn with
0 < p ≤ 1. The ℓ1 -ball, denoted B1n , is an especially useful signal structure due to
its convexity, see, e.g., [45].
To see the connection between sparsity and the ℓ1 -ball, consider an s-sparse vector x, with
√ norm bounded
√ by 1. Then, by Cauchy-Schwartz inequality,
√ Euclidean
kxk1 ≤ s kxk2 ≤ s and thus x ∈ sB1n . However, one may take a slight perturbation of x, without significantly changing the ℓ1 norm, and thus the set accommodates
approximately sparse vectors.

10

YANIV PLAN, ROMAN VERSHYNIN, AND ELENA YUDOVINA

√
Let K = sB1n . For simplicity, we compute the global (as opposed to local) mean
width, which is
p
√
w(K) = E sup
hx,
gi
=
2
s
E
kgk
≤
4
2s log n.
∞
√
x∈2 sB1n

√
The last inequality comes from the well-known fact E kgk∞ ≤ 2 2 log n. Plugging
into (2.1) gives
h η √s log n i1/2
2σ
√
E kb
x − µx̄k2 ≤ √ + 16
.
m
m
Thus, m ∼ s log n observations are sufficient to estimate an approximately s-sparse
vector in Rn .
In Section 4.3 we give a careful treatment of the ℓ1 -ball using local mean width.
We will do a (well known) calculation showing that this improves the above error
bound slightly (roughly, log n can be replaced by log(2n/s)). We will find that the
true error rate is minimax optimal for many models
√ of the functional dependence
of yi on hai , xi when m is not too large (m ≤ C n). We emphasize the unusual
dependence m−1/4 in this non-asymptotic regime.
Approximately sparse in a dictionary. Consider a dictionary D (see Section
2.4). By replacing B1n , with DB1n , one has a set encoding approximate sparsity in
the dictionary D. Fortunately, due to its convexity, projection onto this set may
be performed efficiently. Further, it is straightforward to bound the mean width.
Indeed, Slepian’s inequality [35] gives
p
w(DB1n ) ≤ kDk · w(B1n ) ≤ 4kDk 2s log n.
In other words, if the operator norm of D is bounded, then the set DB1n has similar
essential dimension—and therefor error bound—as the set B1n . Summarizing, we
find that m ∼ s log n observations are sufficient to estimate a vector that is s-sparse
in a dictionary of n elements.
3. Observations yi : consequences and examples
We specialize our results to some standard observation models in this section.
3.1. Linear observations. The simplest example of yi are linear observations
The parameters in (1.2) are then
µ = kxk2 ,

yi = hai , xi .
σ=

√

2 kxk2 ,

(3.1)
η = kxk2 .

Substituting them into (1.7) and (2.4), we obtain the following result.
Corollary 3.1 (Estimation from linear observations). Assume that x ∈ K where
K is a star-shaped set in Rn . Assume the observations yi are given by (3.1). Then
for every t > 0,
2 kxk h√
wt (K) i
.
E kb
x − xk2 ≤ t + √ 2 2 + η
m
t
If K is a cone, then
w1 (K)
E kb
x − xk2 ≤ C √
kxk2
m
√
where C = 2( π + 1) ≈ 5.54.

HIGH-DIMENSIONAL ESTIMATION WITH GEOMETRIC CONSTRAINTS

11

Note that here we estimate the signal x itself, rather than its scaled version as in
our previous results.
3.2. Noisy linear observations. A more general class of examples includes noisy
linear observations of the form
yi = hai , xi + εi

(3.2)

ν 2,

where εi are mean-zero, variance
random variables which are independent of
each other and of ai . Clearly, such observations follow the single-index model (1.1).
A straightforward computation of the parameters in (1.2) shows that
q
q
√
µ = kxk2 , σ = 2kxk22 + ν 2 ≤ 2 kxk2 + ν, η = kxk22 + ν 2 ≤ kxk2 + ν.
Substituting them into (1.7) and (2.4), we obtain the following result.

Corollary 3.2 (Estimation from noisy linear observations). Assume that x ∈ K
where K is a star-shaped subset of Rn . Assume that the observations yi are given
by (3.2). Then
kxk + ν h
wt (K) i
1+
(3.3)
E kb
x − xk2 ≤ t + C √2
m
t
√
where C = 2 2 ≈ 2.83. If K is a cone then
w1 (K)
E kb
x − xk2 ≤ C ′ (kxk2 + ν) √
m

√
where C ′ = 2( π + 1) ≈ 5.54.

Remark 3.3 (Signal buried in noise). One observes that both the size of the signal,
kxk2 , and the size of the noise, ν, contribute to the error bound. When the noise
is larger than the signal, the estimate may still be quite accurate because of the
dimension reduction gained by projecting onto K. In fact, we will show that the
error is minimax optimal up to a multiplicative constant under general conditions
on K in Section 4. When the signal is larger than the noise, the proposed estimator
may not be optimal, depending on K. Indeed, if K = Rn , m ≥ n, and ν = 0,
the minimax error is 0. However, as a general theme in this paper we concentrate
on rough observations for which 0 error is impossible—noiseless linear observations
being the exception to this rule. The case of noiseless linear observations are handled
well by the generalized Lasso estimator, as analyzed in the follow-up paper [46]. In
that case, the size of the signal does not appear in the error bound.
Remark 3.4 (Generalizing assumptions). We also note that the corollary could be
adapted to the case when the random variables εi expressing the noise are not mean
zero, and do depend on x (but only through hai , xi).
3.3. Nonlinear observations. A general class of examples satisfying the singleindex model (1.1) consists of nonlinear observations of the form
yi = f (hai , xi).

(3.4)

Here f : R → R is a fixed link function, which may be unknown. In particular, f may
be discontinuous and not one-to-one (like the sign function), or even nonmonotonic
in its argument (like the sine function).

12

YANIV PLAN, ROMAN VERSHYNIN, AND ELENA YUDOVINA

The rotation invariance of ai allows us to express the parameters in (1.2) as
functions of f and kxk2 only. Indeed,


µ = E f (gkxk2 )g, σ 2 = Var f (gkxk2 )g , η 2 = E f (gkxk2 )2 ,
(3.5)

where g ∼ N (0, 1). Substituting this into Theorem 1.3 or Theorem 2.1, we can
bound the error of estimation in terms of f , the magnitude of signal kxk2 , and the
local mean width of K; we are leaving this to the interested reader.
It is important and surprising that our estimation procedure—computing x
b from
the observations yi —does not depend on the function f . In words,
Any knowledge of the non-linearity f defining the observations yi is
not needed to estimate x from these observations.
The quality of estimation must of course depend on f , and the exact dependence is
encoded in the parameters µ, σ and η in (3.5). However, one does not need to know
f exactly to compute reasonable bounds for these parameters.
Roughly, σ and η, which play a similar role to a noise level, are well upper bounded
if f (g) does not have extremely heavy tails. On the other hand, µ which plays a role
similar to signal strength, can be lower-bounded when f is monotonically increasing
(although this is not necessary). A notable exception is when f is an even function
and µ = 0. In that case, the method of signal estimation described in this paper
would be inappropriate. In Section 4.2 we give a more precise treatment bounding
these parameters, and thus derive uniform error bounds for a fairly large class of
functions f .
3.4. Generalized linear models. An even more general class of examples for the
single-index model (1.1) consists of the noisy non-linear observations
yi = f (hai , xi + εi ) + δi .
Here εi are random variables independent of each other and of ai , and the same is
assumed about δi . (However, εi may be correlated with δi .) This is a variation on
the generalized linear model, which assumes
3

E[yi |ai ] = f (hai , xi)

for some known function f . In contrast, we take the semiparametric approach and
assume that the non-linearity, f , is unknown. (On the other hand, GLMs typically
assume more general noise models than we do; we discuss the connections between
single-index models and GLMs further in Section 6.) To specialize Theorem 1.3 to
this case, we need only control µ, σ, and η.
3.5. Binary observations and logistic regression. An important class of examples is formed by binary observations—those satisfying the model (1.1) and such
that
yi ∈ {−1, 1}n , E yi = f (hai , xi).
(3.6)
Denoting g ∼ N (0, 1) (the standard normal variable), we can compute the parameters in (1.2) as follows:
µ = E f (gkxk)g,

σ 2 ≤ 1,

η = 1.

(The computation for σ follows by replacing the variance by the second moment.)
Substituting this into (2.4), we obtain the following result.
3The generalized linear model also usually assumes that y belongs to an exponential family.
i

HIGH-DIMENSIONAL ESTIMATION WITH GEOMETRIC CONSTRAINTS

13

Corollary 3.5 (Estimating from binary observations). Assume that x ∈ K where
K is a fixed cone in Rn . Assume the observations yi satisfy (1.1) and (3.6). Let
x̄ = x/kxk2 . Then
w1 (K)
E x
b − µx̄ 2 ≤ C √
m
√
where µ = E f (gkxk)g and C = 2π − 4 + 2 ≈ 3.51.
Binary observations are important in both signal processing and statistics. We
outline the connections between our model and the literature below.
1-bit compressed sensing. The noiseless 1-bit compressed sensing model [6]
takes observations
yi = sign(hai , xi).
p
These form a particular case for which Corollary 3.5 applies with µ = E |g| = 2/π.
This type of observations is motivated as a way of understanding the combination
of extreme quantization with low-dimensional, or structured signals. The s-sparse
signal set described in Section 2.4 is of key interest. We note that the magnitude
of x is completely lost in the observations (even with the non-linearity known), and
thus it is standard in this literature to estimate the direction x̄. We may normalize
our estimator to give
r
s log(2n/s)
x
b
− x̄ ≤ C
.
E
kb
xk2
m
2
This recovers the following result from the literature (see [45, 29]): m ∼ s log(2n/s)
1-bit observations are sufficient to estimate an s-sparse vector in Rn .

Logistic regression. Logistic regression is a common statistical model for binary
data, and takes the form
yi = sign(hai , xi + εi )
where εi is logit noise. We note that other forms of noise lead to other binary
statistical models. For example, if εi is Gaussian, this recovers the probit model.
There is a recent influx of statistical literature on combining sparsity with binary
observations, see [4] and references therein. The standard method is ℓ1 -penalized
maximum likelihood estimation. To perform maximum likelihood estimation, it is
vital to know the likelihood function—this is equivalent to knowing the form of the
noise. However, in practice, given binary observations, it is often unclear which
binary model to choose, and one is chosen arbitrarily. In the present paper we
emphasize that estimation can be done accurately without precise knowledge of
relationship between hai , xi and yi .
4. Optimality
In this section we determine general conditions under which the projection estimator gives an optimal result up to a numerical constant, i.e., there is no estimator
which significantly improves on the projection estimator. We begin by considering the noisy linear model described in (3.2). This will automatically give a lower
bound in the case when the observations include a non-linearity. We will come to
the following intriguing conclusion.

14

YANIV PLAN, ROMAN VERSHYNIN, AND ELENA YUDOVINA

When the measurements are noisy, an unknown, noninvertible nonlinearity in the measurements often does not significantly decrease
one’s ability to estimate the signal.
4.1. Lower bound in the linear model. We begin by considering the linear
model with Gaussian noise
yi = hai , xi + εi
(4.1)
2
where the noise variables εi ∼ N (0, ν ) are independent of each other and the ai .
(The parameter ν is the level of noise.) For compact notation, let y ∈ Rm be the
vector of observations and A ∈ Rm×n be the matrix whose i-th row is aT
i .
Our goal is to determine conditions on K and the noise which imply that the
projection estimator is minimax up to a numerical constant. For simplicity, we refer
to numerical constants as C if they are greater than 1, and c if they are less than 1.
These constants may change from instance to instance, but each is bounded by an
absolute numerical value. We refer to the projection estimator (1.6) as x
bproj in this
section.
The local packing number, which we define as follows, plays a key role in estimation
error.
Definition 4.1 (Local packing number, Pt ). Given a set K ⊂ Rn , the local packing
number Pt is the packing number4 of K ∩ tB2n with balls of radius t/10.
We now give a lower bound on the minimax error in the noisy linear model.
Theorem 4.2. Assume that x ∈ K where K is a star-shaped subset of Rn . Assume
that the observations yi are given by 4.1. Let

i
p
ν h
1 + log Pt .
(4.2)
δ∗ := inf t + √
t>0
m

Then there exists an absolute constant c > 0 such that any estimator x
b which depends
only on the observations yi and measurements ai satisfies
sup E kb
x − xk2 ≥ c min(δ∗ , diam(K)).

x∈K

Let us compare this to the upper bound achieved by the projection estimator, as
derived in Corollary 3.2:


ν + kxk2 h
wt (K) i
.
1+
E kb
xproj − xk2 ≤ C inf t + √
t>0
t
m

We now determine conditions under which the two match up to a constant. Observe
that the two match most closely when the noise is large. In particular, when ν ≥
kxk2 , we have the simplified error bound:


wt (K) i
ν h
′
=: C ′ δ∗ .
E kb
xproj − xk2 ≤ C inf t + √ 1 +
t>0
t
m

Further, since the estimator projects onto K, the upper bound may be tightened:
E kb
xproj − xk2 ≤ C ′ min(δ∗ , diam(K)).

4Given a set K ⊂ Rn and a scalar t > 0, a packing of K with balls of radius t is a set X ⊂ K

satisfying kv − wk2 ≥ t for each pair of distinct vectors v, w ∈ X . The packing number is the
cardinality of the largest such packing.

HIGH-DIMENSIONAL ESTIMATION WITH GEOMETRIC CONSTRAINTS

15

√
The only difference between lower and upper bounds is that log Pt is replaced by
wt (K)/t. While these quantities may be in general different, for many sets they are
comparable. To compare them, let us introduce the following geometric parameter:
Definition 4.3 (Ratio: packing to local mean width). Define α as
wt (K)
α = α(K) = sup √
.
t>0 t log Pt

(4.3)

It follows by definition of α that the lower bound of Theorem 4.2 satisfies
δ∗ ≥ δ∗ /α.

The following corollary follows immediately.

Corollary 4.4. Assume that x ∈ K where K is a star-shaped subset of Rn . Assume
that the observations yi are given by 4.1. Let

ν h
wt (K) i
.
(4.4)
δ∗ := inf t + √ 1 +
t
t
m
Then any estimator x
b satisfies
sup E kb
x − xk2 ≥ cα min(δ∗ , diam(K)),

x∈K

where cα = c/α for the numerical constant c defined in Theorem 4.2 and α defined
in (4.3).
Thus, in the high-noise regime, the difference between upper and lower bounds is
a factor of (numerical constant times) α. Fortunately, for many sets of interest, α is
itself bounded (on both sides) by a numerical constant. In particular, this holds true
for sparse vectors, low-rank matrices and the ℓ1 -ball, as described in Section 4.3.
Thus, in this case, the minimax error satisfies
c min(δ∗ , diam(K)) ≤ inf sup E kb
x − xk2 ≤ C min(δ∗ , diam(K)).
x
b x∈K

Remark 4.5 (Defining α at scale). If desired, α can be tightened by defining it at
the relevant scale. Let δ2∗ = 21 δ∗ . Then one can redefine α as
α(K) =

wδ2∗ (K)
p
log Pδ2∗

δ2∗

and the result of the Corollary still holds.

(4.5)

Remark 4.6 (General measurement vectors). Theorem 4.2 and Corollary 4.4 hold
for a general class of random (or deterministic) measurement vectors. The theory
only requires that A does not stretch out signals too much in any one direction, that
is,
1
k E A∗ Ak ≤ 1.
m
1
Note that for the special case of a Gaussian measurement matrix m
E A∗ A = In and
the above requirement is met; similarly if A contains i.i.d. entries with variance 1,
or, more generally, if the rows are in isotropic position. If one wishes to generalize
further, a look at the proof implies that we only require
kAxk2
≤ 1.
E sup
x∈K−K kxk2

16

YANIV PLAN, ROMAN VERSHYNIN, AND ELENA YUDOVINA

Further, the number 1 on the right-hand side could be replaced by any numerical
constant without significantly affecting the result.
Remark 4.7 (Signal-to-noise ratio assumption). Our optimality assumption requires
kxk2 ≤ ν, which places a bound on the norm of the signal. To give a precise minimax
accounting of the error, this assumption can be incorporated into Theorem 4.2 by
replacing K by K ∩ νB2n . In this case, the theorem implies the lower bound
E kb
x − xk2 ≥ c min(δ, diam(K), ν).
Of course, if ν is known then this information can be taken into account in the
projection estimator, giving it a mirroring error bound. One may check that the
geometric assumption on K need not be adjusted. This follows from a rescaling
argument and since K is star-shaped.
4.2. Optimality in the non-linear model. Now we extend our discussion of
optimality to non-linear observations. Let us now assume that the noisy linear data
is passed through an unknown non-linearity:
yi = f (hai , xi + εi )

(4.6)

where εi ∼ N (0, ν 2 ). Note that the non-linearity can only decrease one’s ability to
estimate x, and thus our lower bound still holds. It remains to determine conditions
under which our upper bound matches. For easiest comparison, we rescale Theorem
1.3 to give the reconstruction error in estimating x.
Theorem 4.8 (Non-linear estimation rescaled). Let λ := kxk2 /µ, so that E λb
xlin =
′
λµx̄ = x. Assume that x ∈ K = λK where K is a fixed star-shaped closed subset
of Rn . Then the non-linear estimator x
b defined in (1.6) satisfies the following for
every t > 0:
wt (K ′ ) i
2λ h
.
(4.7)
E kλb
x − xk2 ≤ t + √ σ + η
t
m
Here µ, σ and η are the numbers defined in (1.2).
Proof. The unscaled version, Theorem 1.3, gives
2 h
wt (K) i
E kb
x − µx̄k2 ≤ t + √ σ + η
.
t
m

Now multiply both sides of the inequality by λ, substitute t for λt, and note that
wλt (λK) = λwt (K)
to complete the rescaling argument.



Comparing to the estimation error from noisy linear observations (3.3), we find
that the non-linearity increases the error by at most a constant factor provided
λ(σ + η) ≤ C(kxk2 + ν).
Below, we give some general conditions on f which imply this inequality for all
signals x and noise levels ν.
Lemma 4.9 (Conditions on the non-linearity). Suppose that f is odd and nondecreasing. Further, suppose that f is sub-multiplicative on R+ , that is f (a · b) ≤

HIGH-DIMENSIONAL ESTIMATION WITH GEOMETRIC CONSTRAINTS

17

f (a) · f (b) for all a, b > 0. Then, for the model defined in (4.6), the parameters
λ = µ−1 kxk2 , σ, η defined in (1.2) and the noise level ν satisfy
λ(σ + η) ≤ Cf (kxk2 + ν),

where

(4.8)

Cf = C E[f 4 (g)]1/4 , C = 481/4 P {|g| ≥ 1} ≈ 1.8.
with g ∼ N (0, 1). In particular, Cf does not depend on kxk2 or ν.
Below are a few examples of non-linearities f that satisfy the conditions of
Lemma 4.9:
(1) The identity function, f (x) = x; Cf ≈ 2.36.
(2) The sign function, f (x) = sign(x); Cf ≈ 1.8.
(3) Monomials of the form f (x) = xk for some odd natural number k; Cf ≈
1.8M4k , where M4k = [(4k − 1)!!]1/4k is the 4kth moment of the standard
normal distribution.
Remark 4.10 (Linear combinations). Note that the left-hand side of (4.8) remains
unchanged if f is multiplied by a constant. Moreover, take k functions f1 , . . . , fk
satisfying the conditions of Lemma 4.9, and consider a nonlinear model of the form
yi =

k
X

Cj fj (hai , xi + εi ),

j=1

Cj > 0 ∀j

where all Cj are positive (so that Cj fj is still monotonic). It is not hard to see that
λ, σ, η, and ν satisfy
√
λ(σ + η) ≤ k max Cfj (kxk2 + ν),
j

where Cfj are defined as in Lemma 4.9. This allows us to obtain uniform error
bounds for finite linear combinations of any of the functions satisfying the conditions
of Lemma 4.9, such as odd nondecreasing polynomials of degree at most k.
We summarize our findings in the following corollary:
Corollary 4.11 (Summary or optimality results). Consider the nonlinear model
P
(4.6), and suppose the nonlinearity takes the form f = kj=1 Cj fj , where the functions fj are odd, nondecreasing, and sub-multiplicative. Let Cfj be the constants
defined in Lemma 4.9. Let the signal set K satisfy α(K) < C1 , for α defined in
(4.5). Suppose the noise level ν is comparable to the signal level kxk2 : ν ≥ c2 kxk2 .
Define

wt (K) i
ν h
.
δ∗ = inf t + √ 1 +
t
m
t
Let x
bproj be the projection estimator defined in (1.6), and let x
bopt be the optimal
estimator of x. Then there exists an absolute numeric constant c, and constants
C(fj ) depending only on fj , such that
c2
min(δ∗ , diam(K))
kb
xopt − xk22 ≥ c
C1
and
k
√ X
kb
xproj − xk22 ≤ k(
Cf2j ) min(δ∗ , diam(K)).
j=1

18

YANIV PLAN, ROMAN VERSHYNIN, AND ELENA YUDOVINA

4.3. Sets satisfying the Geometric Condition. In this section we show that for
the three commonly used signal sets discussed in Sections 2.4–2.6, the relationship
w (K)
√t
≤C
(4.9)
t log Pt
holds for all resolutions t (with t < 1 in the case of the ℓ1 -ball), for some universal
constant C. Thus, α(K) ≤ C for these sets. The condition (4.9) is essentially
asserting that Sudakov’s minoration inequality (see [35, Theorem 3.18]) is reversible
for (K − K) ∩ tB2n at scale t/10. Indeed, applying Sudakov’s minoration inequality
to the Gaussian process√Xx = hg, xi, g ∼ N (0, In ), defined on (K − K) ∩ tB2n shows
E sup Xx = wt (K) ≥ ct log Pt for some numeric constant c. (Note that the packing
number of (K − K) ∩ tB2n is at least as great as the packing number of K ∩ tB2n .)

Sparse vectors. Let Sn,s ⊂ Rn be the set of s-sparse vectors, i.e., the set of
vectors in Rn with at most s nonzero entries. Note that Sn,s is a cone, so if (4.9) is
satisfied for Sn,s at some level t, then it is satisfied at all levels. Thus, without loss
of generality we take t = 1.
For K = Sn,s we have K − K = Sn,2s . As shown in Section 2.4,
2n
,
w12 (K) = w2 (Sn,2s ∩ B2n ) ≤ Cs log
s
for some constant C. On the other hand, we show that there exists a 1/10-packing
X1 of Sn,s ∩ B2 such that
r
2n
,
log |X1 | ≥ c s log
s
for some (possibly different) constant c. Such a packing exists when s > n/4,
because then Sn,s contains an (n/4)-dimensional unit ball, whose packing number
is exponential in n. Thus, we may assume s < n/4. Consider the set Q of vectors x,
where each x ∈ Q has exactly s nonzero coordinates, and the nonzero coordinates
are equal to exactly s−1/2 . Thus, Q ⊂ Sn,s ∩ B2 , and |Q| = ns . Q itself is not the
packing, but we will show that we can pick a large subset X1 of Q such that any
two vectors x, y ∈ X1 satisfy kx − yk2 > 1/10.
Consider picking vectors from Q uniformly at random. For two uniformly chosen
vectors x, y ∈ Q, we have


o
n
s
1
2
coordinates .
= P x, y disagree on at most
P kx − yk2 ≤
100
100
99
This requires y to have at least 100
s of the same nonzero coordinates as x. Given x,
and assuming without loss of generality that 0.01s is an integer (rounding will not
make a significant effect in the end), this happens for exactly



 
s
n − 0.99s
n
out of
0.99s
0.01s
s

values of y ∈ Q. Using Stirling’s approximation (and the assumption s < n/4),




2n
1
≤ exp −Cs log
.
P kx − yk2 ≤
10
s

Now let X1 ⊆ Q contain c exp(s log 2n
s ) uniformly chosen elements of Q. Then with
positive probability kx − yk2 > 1/10 for all pairs x, y ∈ X1 . In particular, there
exists at least one 1/10-packing X1 of Sn,s ∩ B2 of size c exp(s log 2n
s ).

HIGH-DIMENSIONAL ESTIMATION WITH GEOMETRIC CONSTRAINTS

19

Low-rank matrices. Let Md1 ,d2 ,r be the set of d1 × d2 matrices with rank (at
most) r; clearly r ≤ min(d1 , d2 ). This is again a cone, so it suffices to show that (4.9)
is satisfied at a single level t = 1. Now, for K = Md1 ,d2 ,r , we have K −K = Md1 ,d2 ,2r ,
so (see [45, Section 3.3] or Section 2.5 of this paper)
w12 (K − K) = w2 (Md1 ,d2 ,2r ) ∩ B2d1 d2 ) ≤ C(d1 + d2 )r.
For the packing number, note that Md1 ,d2 ,r contains all matrices whose last d2 − r
rows are identically zero, and also all matrices whose last d1 − r rows are identically
zero. Thus, Md1 ,d2 ,r contains a copy of Rd1 r and a copy of Rd2 r . Since the packing
number of a unit ball in Euclidean space is exponential in its dimension, we conclude
for the packing number of K
c
log P1 ≥ c max(d1 , d2 )r ≥ (d1 + d2 )r,
2
as required.
Approximately sparse signals: ℓ1 -ball. For the set of approximately sparse
signals contained in the ℓ1 -ball B1n , we will show that condition (4.9) is satisfied at
all levels t < 1. Note that for K = B1n , K − K ⊂ 2B1n . We first derive precise
bounds on the local mean width of K,
wt2 (B1n ) ≤ w2 (2B1n ∩ tB2n ) ≤ t2 w2 (2t−1 B1n ∩ B2n ).

It is easy to see that, for all t, wt2 (B1n ) ≤ C ′ min(t2 n, log n). The former will apply
when t is very small (t ≤ Cn−1/2 ); the latter will apply when t is very large (t ≥ c).
√
In the intermediate regime Cn−1/2 < t < c, let s = 2t−1 ; then c−2 < s < C −2 n.
Assume for simplicity that s is an integer; rounding s will not affect the results
substantially. Now, by [44, Lemma 3.1]
1 √ n
( sB1 ∩ B2n ) ⊂ conv(Sn,s ∩ B2n ),
2
and by [45, Lemma 2.3],
w2 conv(Sn,s ∩ B2n ) = w2 (Sn,s ∩ B2n ) ≤ C ′ s log
Consequently,


′

C n,
t−2 wt2 (B1n ) ≤ C ′ t−2 log(nt),

 ′ −2
C t log n,

2n
.
s

t ≤ Cn−1/2 ;
Cn−1/2 < t < c;
c ≤ t ≤ 1.

We now consider packings of B1n ∩ tB2n by balls of radius t/10. Clearly, for
t < n−1/2 , the packing number is at least exponential in n, because the set contains
an n-dimensional ball of radius t; and for c < t < 1 the packing number is at least 2n,
because we can simply pack the vertices. It remains to treat the intermediate regime
n−1/2 < t < c. Note√that the problem is equivalent to packing t−1 B1n ∩ B2n by balls
of radius 1/10. Let s = t−1 ; assuming s is an integer, we have Sn,s ⊂ t−1 B1n ∩ B2n ,
so we may apply the packing bounds for sparse vectors:
log |Xt | ≥ cs log

2n
= ct−2 log(nt).
s

20

YANIV PLAN, ROMAN VERSHYNIN, AND ELENA YUDOVINA

It is not hard to put these bounds together to conclude that indeed,


t ≤ Cn−1/2 ;
cn,
log |Xt | ≥ ct−2 log(nt), Cn−1/2 < t < c;

 −2
ct log n,
c ≤ t ≤ 1.

5. Related results: Low M ∗ estimate, Chatterjee’s least squares
estimate

5.1. Low M ∗ estimate from geometric functional analysis. The function
wt (K)
t
that appears in Theorem 1.3 has been studied in geometric functional analysis. It
is known to tightly control the diameter of random sections of K. An upper bound
on the diameter is known as the low M ∗ estimate (see [38, 39, 42, 25]) and lower
bounds have been established in [21, 20, 22, 24]. The following is a simplified version
of the low M ∗ estimate, see [37].
t 7→

Theorem 5.1 (Low M ∗ estimate). There exists an absolute constant c > 0 such
that the following holds. Let K be a star-shaped subset of Rn . Let A be an n × m
random matrix whose entries are independent N (0, 1) random variables. Assume
that t > 0 is such that
√
wt (K)
≤ c m.
t
Then with probability at least 1 − exp(−cm), we have
ku − vk2 ≤ t

for any u, v ∈ K, u − v ∈ ker(A).

(5.1)

The conclusion (5.1) can be interpreted in a geometric way as follows. All sections
of K parallel to the random subspace E = ker(A) have diameter at most t, i.e.

diam K ∩ (E + v) ≤ t for all v ∈ Rn .

The relevance of the low M ∗ estimate to the estimation problems was first observed in [37]. Suppose one wants to estimate a vector x ∈ K from linear observations as in Section 3.1, i.e.
yi = hai , xi ,

i = 1, . . . , m.

Choose an arbitrary vector x
b0 ∈ K which is consistent with the observations, i.e.
such that
hai , x
b0 i = yi , i = 1, . . . , m.
For convex feasible sets K, computing a vector x
b0 this way is an algorithmically
tractable convex feasibility problem.
To bound the error of this estimate, we can apply the low M ∗ estimate (5.1) for
A the m × n matrix with rows aT
b0 ∈ K and x − x
b0 ∈ ker(A), it follows
i . Since x, x
that with high probability, kb
x0 − xk2 ≤ t. To summarize,

√
wt (K)
≤ c m implies kb
x0 − xk2 ≤ t with high probability.
(5.2)
t
In particular, if K is a cone then wt (K)/t = w1 (K), and we can let √
t → 0. In this
case, the low M ∗ estimate guarantees exact recovery once w1 (K) ≤ c m.

HIGH-DIMENSIONAL ESTIMATION WITH GEOMETRIC CONSTRAINTS

21

To compare (5.2) with the error bound of the non-linear estimation, we can state
the conclusion of Theorem 1.3 as follows:
√
wt (K)
2σ
≤ ε m implies kb
x − xk2 ≤ t + 2ε + √ with high probability.
t
m
The two additional terms in the right hand side can be explained by the fact that
the the exact recovery is impossible in the single-index model (1.1), in particular
because of the noise and due to the unknown non-linear dependence of yi on hai , xi.
5.2. Chatterjee’s least squares under convex constraint. As this paper was
being written, [13] proposed a solution to a closely related problem. Suppose that
an unknown vector x ∈ Rn lies in a known closed convex set K. We observe the
noisy vector
y = x + g, where g ∈ N (0, In ).
We would like to estimate x from y. This is very similar to the linear estimation
problem (3.2), which can be written in the form y = Ax + g. An important difference is that in Chatterjee’s model, A is the identity matrix; so one needs to take
n observations of an n-dimensional vector x (given as coordinates yi of y). In contrast, the current paper assumes that A is an m × n Gaussian matrix (essentially a
projection), so one is allowed to take m observations where usually m ≪ n.
The estimator proposed in [13] is the least-squares estimator, which clearly equals
the metric projection of y onto K, that is
x
b = PK (y).

Note that this coincides with the second step of our estimator. (The first step—a
linear estimator—is clearly not needed in Chatterjee’s model where A is identity.)
The performance of the least-squares estimator is quantified in [13] using a version
of the local mean width of K; this highlights one more similarity with the current
paper.
6. Connections to statistics and econometrics
The single-index model we consider is widely used in econometrics; the monograph of [27] gives a good introduction to the subject. In this section, we discuss
connections and differences between our method and some of the literature. For
this section only, we will use notation more common in statistics literature; Table 1
below summarizes the translation between the two worlds.
Table 1. Dictionary of notation
Quantity
independent variable
observation
index
dimension of parameter space
number of observations

Rest of paper This section
ai ∈ R n
yi ∈ R
x ∈ Rn
n
m

Xi ∈ Rp
Yi ∈ R
β ∈ Rp
p
n

We note that much of the work on single-index models considers a broader formulation, namely
Yi = f (hβ, Xi i) + εi ,
E[εi |Xi ] = 0.

22

YANIV PLAN, ROMAN VERSHYNIN, AND ELENA YUDOVINA

Our work relies crucially on the additional structural assumption that εi and Xi are
conditionally independent given hβ, Xi i.
The single-index model is similar to generalized linear models (GLMs) in regression analysis, and contains many of the widely used regression models; for example,
linear, logistic, or Poisson regressions. Our results apply directly to those models,
with the added advantage that we do not need to know in advance which of the
models is appropriate for a specific dataset. (Note especially that Corollary 4.11
guarantees uniform error bounds e.g., for most cubic functions f .) In addition,
Section 3.5 demonstrates the optimality of our approach for a rather general model
of sparse binary-response data, which encompasses the logit or probit models for
specific distributions of εi .
However, we remark that there is no containment between single-index models and
GLMs; for example, the negative binomial model is a commonly used GLM which
does not fit in the single-index framework. (There is also no reverse containment, as
single-index models do not require the distribution of Yi to come from an exponential
family.)
The literature on single-index and more general semiparametric models is large
and growing, so we can only outline some of the connections here. The primary
appeal of single-index models to statisticians has been that it allows estimation of
√
β at the parametric rate ( n(β̂n − β) is tight) as well as estimation of f at the
same rate as if the one-dimensional input hβ, Xi i were observable. Estimation of an
unknown function is notoriously difficult when the domain is high-dimensional, so
the latter feature is particularly attractive when p is large. We note that much of the
analysis of these results is focused on the asymptotic rates of convergence, although
some recent papers [28, 2, 17] also provide nonasymptotic (finite n) guarantees.
However, in contexts where the primary concern is with the index β, the classical
approaches (such as [52, 47, 28]) can be unduly restrictive with regards to the link
function. This is because they tend to be based on the average derivative method,
i.e. the observation that
∂ E[Y1 |X1 = x]
= Cβ.
∂x
Naturally, methods based on this idea require the link function f to be (at least)
differentiable, whereas we can allow f (·) = sign(·).
To our knowledge, the only fundamentally different approach to recovery of β
alone (without f ) was taken by Li and Duan [36]. The authors considered observations Xi with an elliptically symmetric distribution (e.g., correlated normal). They
then demonstrated under some mild conditions that any method based on minimization of a convex criterion function (e.g., least squares) would achieve one of its
minima at β ∗ ∝ β. Thus, essentially any method for which the minimizer happens
to be unique can be used to consistently recover (a multiple of) β; under some ad√
ditional assumptions, n(β̂n − β) is asymptotically normal. The major advantage
of this method is that it does not in any way rely on the smoothness of the link
function f .
The above discussion applies to low-dimensional settings, in which all that is
known about β is β ∈ Rn (perhaps with kβk2 = 1). Recently, a lot of work has been
devoted to the analysis of high-dimensional data (p > n) with additional structural
assumptions about β, for example sparsity. In the case of linear link function f ,
sparse high-dimensional regression is well-studied (see [7] and references therein).

HIGH-DIMENSIONAL ESTIMATION WITH GEOMETRIC CONSTRAINTS

23

On the other end of the complexity spectrum, the work of Comminges and Dalayan
[15] considers a sparse nonparametric model
yi = f (Xi ) = fJ ({(Xi )j : j ∈ J}),

|J| ≤ s

where at most s of the components of the Xi are relevant to the model. They ask
the question of when the set J of relevant components can be recovered, and find
that the necessary number of measurements is
n ≥ C1 exp(C2 s) log(p/s).
We compare this to our result on recovering a single index β belonging to the cone
K of s-sparse vectors, for which
(w1 (K))2 ≤ s log(p/s).
We see that going from a single-index to a nonparametric model involves an exponentially larger number of measurements (but the number is exponential in the
underlying sparsity, not in the full dimension of the parameter space).
We mention also some of the recent work on the estimation of the link function
f in high-dimensional settings. Alquier et al. [2] and Cohen et al. [14] consider
the model of a link function f which depends on at most s of the components of x.
More generally, Dalayan et al. [17] allow all link functions that can be decomposed
as the sum of at most m “nice” functions fV , with each fV depending on at most
s coordinates of x. (A special case of all of these models, including ours, is f (x) =
f˜(hx, βi), where β s-sparse and f˜ comes from a nice function class.) The recent
work of Dalayan et al. [17] obtain general bounds on the quality of estimation of f ,
uniformly over classes containing fV .
6.1. Connection to statistical learning theory. In statistical (supervised) learning theory, the data is used to determine a function that can be used for future
prediction. Thus the data {ai , yi , i = 1, . . . , m} is used to generate a function which
can predict ym+1 given am+1 . Such a predicting function is usually sought in a given
function class. A main focus of the research is to develop oracle inequalities, which
state that the predicting function estimated from the data performs nearly as well
as the optimal function in that class.
By restricting to classes of linear functionals, such results can (with some work)
be specialized and translated to give error bounds on the estimation of x, as in
our paper. We focus on the results of [33] which gives general error bounds under
mild conditions. Nevertheless, there are important differences between these and
the results of our paper. First, statistical learning theory concentrates on empirical
risk minimization [33], which, when combined with a squared loss function, and
specialized to linear functionals, recovers a generalized version of the Lasso. In
contrast, we consider a simpler, and often more efficient, projection method in this
paper. (We note in passing that, after writing this paper, the first two authors
studied the behaviour of the Lasso under the single-index model [46].) Furthermore,
we make especially mild assumptions on the data. In contrast to [33], our Theorem
1.3 does not require yi to be sub-Gaussian. Instead, we roughly only require yi
to have finite second moment so that the parameters µ, σ and η are well defined.
On the other hand, the Lasso can return an exact solution in the noiseless model,
whereas the project-based estimator proposed in this paper always has some error

24

YANIV PLAN, ROMAN VERSHYNIN, AND ELENA YUDOVINA

attached. The Lasso can also tolerate the case when the measurement vectors are
anisotropic [46].
We also point to the work of [40] which can also be specialized to linear functionals to give an error bound similar to the one in our paper. However, the simple
projection method espoused in our paper is often more computationally efficient
than the optimization programs suggested in [40]. Further, the conditions on the
signal structure are much milder. Indeed, in contrast to [40], there is no need for
decomposability or restricted strong convexity.
Finally, we note that the behavior of the generalized Lasso under the linear model
is well studied, and error bounds are known with sharp constants [41]. Recently,
building on the work of [46], sharp constants have been given for the non-linear
model in the asymptotic regime [54]. It is an interesting open question to see if this
can also be done in the non-asymptotic regime.
7. Discussion
We have analyzed the effectiveness of a simple, projection-based estimator in the
semiparametric single index model. We showed that by projecting onto the set
encoding the signal structure, rough, unspecified observations could be combined to
give an accurate description of the signal. The gain from this dimension reduction
was described by a geometric parameter—the mean width. When the noise is large
and under mild assumptions on the rest of the model, we showed that the estimator
is minimax optimal up to a constant. We came to the surprising conclusion that an
unknown, noninvertible non-linearity often does not significantly effect one’s ability
to estimate the signal, aside from a loss of scaling information and an extra numerical
constant in the error bound.
By comparing to what was known in the classic literature on the semiparametric
single-index model, we believe our results a) give a simple method of testing whether
estimation is possible based on easily computable parameters of the model, b) allow for non-invertible, discontinuous, and unknown linearities and c) give a careful
accounting of the benefit of using a low-dimensional signal structure.
Nevertheless, our model takes the measurement vectors to be standard normal,
and it is important to understand whether this assumption may be generalized, and
whether the theory in this paper matches practice. We discuss this challenge in the
rest of this section. We pause to note that the lower bounds take a very general
model for the measurement vectors, which may even be deterministic, thus it is the
model used for the upper bounds which requires generalization.
There is a simple key observation that gives the first step to the theory in this
paper: The linear estimator described in Section 1.2 is an unbiased estimate of the
signal, up to scaling. It may be surprising at first that this holds regardless of a
non-linearity in the observations. This fortunate fact follows from the Gaussianity
of the measurements, as described in the next section.
It is straightforward to generalize the theory to the case ai ∼ N (0, Σ), provided
Σ is known or can be estimated. One only needs to multiply the linear estimator
by Σ−1 to give an unbiased estimator; as long as Σ is well-conditioned, our theory
remains essentially unchanged. The next question is the applicability of other random measurement ensembles. We see two avenues towards this research: 1) As we
illustrated with the matrix completion example of Section 2.5, if the observations

HIGH-DIMENSIONAL ESTIMATION WITH GEOMETRIC CONSTRAINTS

25

are linear, then the linear estimator can be unbiased for non-Gaussian measurement vectors. All that is needed is for the measurement vectors to be in isotropic
position, that is, E ai a∗i = In . This is a common assumption in the compressed
sensing literature [9]. Of course, a slight non-linearity can be accounted for by a
Taylor series approximation. 2) A central idea in high-dimensional estimation is
that non-Gaussian measurement vectors often give similar behavior to Gaussian
measurement vectors. For example, this is made explicit with the universality phenomenon in compressed sensing. Such arguments have been applied to a special case
of this semiparametric single-index model in [1] focused on binary observations with
non-Gaussian measurement vectors. We believe that similar comparison arguments
may be applied in the more general setting of this paper.
In conclusion, we emphasize that the assumption of Gaussian measurements has
allowed a very clean theory, with relatively few assumptions in a general model, and
we hope that it can be a first step towards such theory with other models of the
measurement vectors.
8. Proofs of Proposition 1.1 and Theorem 1.3
8.1. Orthogonal decomposition. The proof of Proposition 1.1 as well as of our
main result, Theorem 1.3, will be based on the orthogonal decomposition of the
vectors ai along the direction of x and the hyperplane x⊥ . More precisely, we
express
ai = hai , x̄i x̄ + bi .
(8.1)
The vectors bi defined this way are orthogonal to x. Let us record a few elementary
properties of this orthogonal decomposition.
Lemma 8.1 (Properties of the orthogonal decomposition). The orthogonal decomposition (8.1) and the observations yi satisfy the following properties:
(i) hai , x̄i ∼ N (0, 1);
(ii) bi ∼ N (0, Ix⊥ );
(iii) hai , x̄i and bi are independent;
(iv) yi and bi are independent.
Proof. Properties (i), (ii) and (iii) follow from the orthogonal decomposition and
the rotational invariance of the normal distribution.
Property (iv) follows from a contraction property of conditional independence.
Let us denote by Y ⊥ B the independence of random variables (or vectors) Y and
B, and by (Y ⊥ B) | H the conditional independence Y and B given H. The
contraction property states that (Y ⊥ B) | H and B ⊥ H imply Y ⊥ B. In our
situation, we have (yi ⊥ bi ) | hai , x̄i by the assumption on yi and since bi is uniquely
determined by ai . Moreover, bi ⊥ hai , x̄i by property (iii). The contraction property
yields yi ⊥ bi . This proves (iv).

8.2. Proof of Proposition 1.1. By the identical distribution of ai and using the
orthogonal decomposition (8.1), we have
Ex
blin = E y1 a1 = E y1 ha1 , x̄i x̄ + E y1 b1 .

(8.2)

The first term in the right hand side equals µx̄ by definition of µ. The second term
equals zero since by the independence property (iv) in Lemma 8.1 and and since
E b1 = 0. We proved the first part of the proposition, E x
blin = µx̄.

26

YANIV PLAN, ROMAN VERSHYNIN, AND ELENA YUDOVINA

To prove the second part, we express
m

E kb
xlin − µx̄k22 = E

1 X
Zi
m
i=1

2
2

,

where Zi = yi ai − µx̄ are independent and identically distributed random vectors
with zero mean. Thus
m
1 X
1
E kb
xlin − µx̄k22 = 2
E kZi k22 =
E kZ1 k22 .
(8.3)
m
m
i=1

Using orthogonal decomposition (8.1) again, we can express Z1 as follows:

where
Note that

Z1 = y1 ha1 , x̄i x̄ + y1 b1 − µx̄ = X + Y


X = y1 ha1 , x̄i − µ x̄,

Y = y 1 b1 .

E hX, Y i = 0.
(8.4)
To see this, hX, Y i =
The two terms forming this product
are independent by properties (iii) and (iv) in Lemma 8.1. Moreover, E b1 = 0,
which yields E hb1 , x̄i = 0, and as a consequence, (8.4) follows.
Property (8.4) implies that
[y12 ha1 , x̄i − µy1 ] · hb1 , x̄i.

E kZ1 k22 = E kX + Y k22 = E kXk22 + E kY k22 .

We have E kXk22 = σ 2 by definition of σ and since kx̄k2 = 1. Next, E kY k22 =
η 2 E kb1 k22 by the independence property (iv) in Lemma 8.1 and by definition of η.
Recalling that b1 is a standard normal random variable in the hyperplane x⊥ , we
get E kb1 k22 = n − 1. It follows that
E kZ1 k22 = σ 2 + η 2 (n − 1).

Putting this into (8.3), we complete the proof.



8.3. Metric projection and dual norm. For a subset T of Rn , we define
kxkT ◦ = sup hx, ui ,
u∈T

x ∈ Rn .

(8.5)

It is a semi-norm if T is symmetric. We define also Td = (T − T ) ∩ dB2n .

Lemma 8.2. Let K be an arbitrary subset of Rn and let z ∈ K, w ∈ Rn . Then the
distance
d := kPK (w) − zk2
satisfies the inequality
d2 ≤ 2kw − zkKd◦
Proof. By definition, PK (w) is the closest vector to w in K, so
We write this as

kPK (w) − wk2 ≤ kz − wk2 .

k(PK (w) − z) + (z − w)k22 ≤ kz − wk22 ,
expand the left hand side and cancel the terms kz − wk22 on both sides. This gives
kPK (w) − zk22 ≤ 2 hPK (w) − z, w − zi .

(8.6)

HIGH-DIMENSIONAL ESTIMATION WITH GEOMETRIC CONSTRAINTS

27

The left hand side of (8.6) equals d2 by definition. To estimate the right hand side,
note that vectors PK (w) and z lie in K and they are d apart in Euclidean distance.
Therefore PK (w) − z ∈ (K − K) ∩ dB2n = Kd . Thus the right hand side of (8.6) is

bounded by 2kw − zkKd◦ as claimed.
Corollary 8.3. Let K be a star-shaped set and let z ∈ K, w ∈ Rn . Then for every
t > 0 we have

 2
(8.7)
kPK (w) − zk2 ≤ max t, kw − zkKt◦ .
t
Proof. The proof relies on the following fact, which is well known in geometric
functional analysis.
Claim. For any fixed x ∈ Rn , the function 1t kxkKt◦ is non-increasing in t ∈ R+ .
To prove this claim, we express the function as
1
1
hx, ui = E
sup
kxkKt◦ = E
sup
hx, vi .
t
t u∈(K−K)∩tB2n
v∈ 1 (K−K)∩B n
t

(8.8)

2

Since K is star-shaped, K − K is star-shaped, too. Then the set 1t (K − K) is
non-increasing (with respect to inclusion) in t ∈ R+ . Thus the same is true about
1
n
t (K − K) ∩ B2 . This and the identity (8.8) prove the claim.
To deduce (8.7), denote d = kPK (w) − zk2 . If d ≤ t then (8.7) holds. If d > t, we
apply Lemma 8.2 followed by the claim above. We obtain
2
2
d ≤ kw − zkKd◦ ≤ kw − zkKt◦ .
d
t
This implies (8.7).

8.4. Proof of Theorem 1.3.
8.4.1. Decomposition of the error. We apply Corollary 8.3 for w = x
blin and z = µx̄;
note that the requirement that z ∈ K is satisfied by assumption. We obtain
2
kb
x − µx̄k2 = kPK (b
xlin ) − µx̄k2 ≤ t + kb
xlin − µx̄kKt◦ .
(8.9)
t
1 Pm
Recall that x
blin = m
i=1 yi ai and use the orthogonal decomposition of ai from
(8.1). By triangle inequality, we obtain
m

1 X
◦
kb
xlin − µx̄kKt =
yi hai , x̄i x̄ + yi bi − µx̄
m
Kt◦
i=1

m

1 X
≤
yi hai , x̄i − µ x̄
m
i=1

m

1 X
+
y i bi
m
Kt◦
i=1

Kt◦

=: E1 + E2 . (8.10)

8.4.2. Estimating E1 . Denoting ξi = yi hai , x̄i − µ, we have
m
1 X
ξi · kx̄kKt◦ .
E1 =
m
i=1

tB2n

B2n ,

so kx̄kKt◦ ≤ t. Further, ξi are independent
and x̄ ∈
By definition, Kt ⊆
and identically distributed random variables with zero mean. Therefore
E E12 ≤

σ 2 t2
1
E[ξ12 ] · t2 =
,
m
m

(8.11)

28

YANIV PLAN, ROMAN VERSHYNIN, AND ELENA YUDOVINA

where the last equality follows by the definition of ξ1 above and the definition of σ
in (1.2).
8.4.3. Estimating E2 . We will estimate
m

E E2 = E khkKt◦

1 X
where h :=
y i bi .
m
i=1

Recall that yi and bi are independent by property (iv) in Lemma 8.1. Let us condition on yi . Rotation invariance of the normal distribution implies that
h ∼ N (0, sIx⊥ ) where s2 =

m
1 X 2
yi .
m2
i=1

In order to extend the support of the distribution of h from x⊥ to Rn , we recall the
following elementary and well known fact.
Claim. For a subspace E of Rn , let gE denote a random vector with distribution
N (0, IE ). Let Φ : Rn → R be a convex function. Then
E Φ(gE ) ≤ E Φ(gF ) whenever

E ⊆ F.

To prove the claim, consider the orthogonal decomposition F = E ⊕ L for an
appropriate subspace L. Then gF = gE + gL in distribution, where gL ∼ N (0, IL ) is
independent of gE . Jensen’s inequality yields
E Φ(gE ) = E Φ(gE + E gL ) ≤ E Φ(gE + gL ) = E Φ(gF )
as claimed.
In the notation of the claim, we can estimate the expectation of khkKt◦ (still
conditionally on the yi ’s) as follows:
E khkKt◦ = E ksgx⊥ kKt◦ = s E kgx⊥ kKt◦ ≤ s E kgRn kKt◦ = s · wt (K)
where the last equality follows by the definitions of the dual norm and local mean
width. Therefore, unconditionally we have
E E2 = E khkKt◦ = E[s] · wt (K) ≤ (E s2 )1/2 · wt (K)
1
1/2
η
=
E y12
· wt (K) = √ · wt (K)
m
m

(8.12)

where the last equality follows from the definition of η in (1.2).
It remains to plug estimates (8.11) for E1 and (8.12) for E2 into (8.10), and we
obtain

1 
E kb
xlin − µx̄kKt◦ = E(E1 + E2 ) ≤ (E E12 )1/2 + E E2 ≤ √ σt + η wt (K) .
m

Finally, substituting this estimate into (8.9) and taking expectation of both sides
completes the proof of Theorem 1.3.


HIGH-DIMENSIONAL ESTIMATION WITH GEOMETRIC CONSTRAINTS

29

9. High-probability version of Theorem 1.3
Using standard concentration inequalities, we can complement Theorem 1.3 with
a similar result that is valid with high probability rather than in expectation.
To this end, we will assume that the observations yi have sub-gaussian distributions. This is usually expressed by requiring that
E exp(yi2 /ψ 2 ) ≤ 2 for some ψ > 0.

(9.1)

Basic facts about sub-gaussian random variables can be found e.g. in [55].
To understand this assumption, consider the case when the non-linearity is given
by an explicit function, i.e., yi = f (hai , xi). Since hai , xi is Gaussian, yi will be
sub-Gaussian provided that f does not grow faster than linearly, i.e., provided that
f (x) ≤ a + b|x| for some scalars a and b.
Theorem 9.1 (Non-linear estimation with high probability). Let x̄ = x/kxk2 . Assume that µx̄ ∈ K where K is a fixed star-shaped closed subset of Rn . Assume √
that
observations yi satisfy the sub-gaussian bound (9.1). Let t > 0 and 0 < s ≤ m.
Then the non-linear estimator x
b defined in (1.6) satisfies
4η h
wt (K) i
kb
x − µx̄k2 ≤ t + √ s +
m
t

with probability at least 1− 2 exp(−cs2 η 4 /ψ 4 ). Here µ and η are the numbers defined
in (1.2), and c > 0 is an absolute constant.

Proof. The conclusion follows by combining the proof of Theorem 1.3 and standard
concentration techniques
√ which can be found in [55]. So we will only outline the
argument. Let ε = s/(2 m); then ε < 1/2 by assumption.
First we bound E1 . Since yi are sub-gaussian as in (9.1) and hai , x̄i is N (0, 1), the
random variables ξi are sub-exponential. More precisely, we have E exp(ξi /Cψ) ≤ 2,
where C > 0 denotes an absolute constant here and thereafter. A Bernstein-type
inequality (see [55, Corollary 5.17]) implies that
m

1 X
ξi ≤ εη,
m
i=1

and thus

E1 ≤ εηt,

with probability at least
h
 ε2 η 2 εη  i
 cmε2 η 2 
1 − 2 exp − c min
m
≥
1
−
2
exp
−
,
.
ψ2 ψ
ψ2

(9.2)

(9.3)

In the last inequality we used that η = E yi2 ≤ Cψ by definition of ψ and that ε ≤ 1
by assumption.
P
2
◦
Turning to E2 , we need to bound m
i=1 yi and kgkKt . A similar application of
a Bernstein-like inequality for the sub-exponential random variables yi2 − η 2 shows
that
m
X
yi2 ≤ 4η 2 m
i=1

with probability at least
 cmη 4 
h
 η4 η2  i
,
m
≥
1
−
2
exp
−
.
1 − 2 exp − c min
ψ4 ψ2
ψ4

(9.4)

30

YANIV PLAN, ROMAN VERSHYNIN, AND ELENA YUDOVINA

Next, we bound kgkKt◦ using Gaussian concentration. Since Kt ⊆ tB2n , the function x 7→ kxkKt◦ on Rn has Lipschitz norm at most t. Therefore, the Gaussian
concentration inequality (see e.g. [34]) implies that
√
√
kgkKt◦ ≤ E kgkKt◦ + tε m = wt (K) + tε m
with probability at least

1 − exp(−cε2 m).
2
◦
i=1 yi and kgkKt are bounded as above, then
m
√ 
2η
1  X 2 1/2
kgkKt◦ ≤ √ wt (K) + tε m .
yi
E2 ≤
m
m

Pm

If both

(9.5)

i=1

If also E1 is bounded as in (9.2), then we conclude as in the proof of Theorem 1.3
that
2
4η wt (K)
kb
x − µx̄k2 ≤ t + (E1 + E2 ) ≤ t + √ ·
+ 6ηε.
t
m
t
Recalling (9.3), (9.4) and (9.5), we see that this happens with probability at least
 cmε2 η 2 
 cmη 4 
 c′ mε2 η 4 
2
1 − 2 exp −
−
2
exp
−
−
exp(−cε
m)
≥
1
−
2
exp
−
ψ2
ψ4
ψ4
for an appropriate absolute constant c′ > 0. (Here we used again that η/ψ < 1 and
ε < 1.) The conclusion of Theorem 9.1 follows by definition of ε.

10. Proofs of Theorem 4.2, Corollary 4.4, and Lemma 4.9.
In this section, for a set K ⊂ Rn we use the notation Kt = K ∩ tB2n . (Note that
we do not symmetrize K first.)
10.1. Proof of Theorem 4.2. The theorem is proven with a careful application of
Fano’s inequality, an information theoretic method which is useful for determining
minimax estimation rates. We state a version of Fano’s inequality synthesized for
lower-bounding expected error in the linear model (the steps needed to specialize
Fano’s inequality can be seen in [48, 8], for example). For the general version see
[16]. In all that follows, Xt is a t/10-packing of Kt with maximal cardinality. Thus,
|Xt | = Pt .
Theorem 10.1 (Fano’s Inequality for expected error). Consider a fixed matrix A
in the linear model of Section 4.1, and let t > 0. Suppose that
X
1
1
kA(w − v)k22 + log(2) ≤ log(|Xt |).
(10.1)
2
2
ν |Xt |
2
w6=v

w,v∈Xt

Then there exists a constant c > 0 such that for any estimator x
b(A, y),
sup E kb
x − xk2 ≥ ct.

x∈Xt

Theorem 4.2 will be proven by conditioning on A and applying Fano’s inequality. We begin to control the left-hand side of (10.1) by showing that A does not
significantly increase the average distance between points in our packing. Indeed,
X
X
X
kA(w − v)k22 ≤ k E A∗ Ak
E
k(w − v)k22 ≤ m
k(w − v)k22 ≤ 4m|Xt |2 t2 .
w6=v

w,v∈Xt

w6=v

w,v∈Xt

w6=v

w,v∈Xt

HIGH-DIMENSIONAL ESTIMATION WITH GEOMETRIC CONSTRAINTS

The last step follows since Xt ⊂ tB2n . Thus, by Markov’s inequality,
X
kA(w − v)k22 ≤ 16m|Xt |2 t2
with probability at least 3/4.

31

(10.2)

w6=v

w,v∈Xt

We pause to note that the above inequality is the only property of A that is needed.
By conditioning on the good event that the Inequality (10.2) holds, the following
Corollary gives a significant simplification of Theorem 10.1.
Corollary 10.2 (Simplified Fano Bound). Consider
√ the linear model of Section 4.1,
suppose k E AA∗ k ≤ m, and let t > 0. Set ν̄ := ν/ m. Suppose that

t2
+ 1 ≤ c log(|Xt |)
(10.3)
ν̄ 2
for a small enough constant c. Then there exists a constant c′ > 0 such that for any
estimator x
b(A, y),
sup E kb
x − xk2 ≥ c′ t.
x∈Xt

We are now in position to prove Theorem 4.2.

Proof. Let c0 be a small numerical constant, whose value will be chosen at the end
of the proof, and let
h
i
p

δ := c0 δ∗ = c0 inf t + ν̄ 1 + log Pt .
(10.4)
t

We split the proof into two cases, depending on the relative size of δ and ν̄.
Case 1: δ ≥ ν̄.
This case contains the meat of the proof, but nevertheless it can be proven quite
quickly with the tools we have gathered. Since δ is the infimum in (10.4), it satisfies
i

h
p
δ ≤ c0 δ + ν̄ 1 + log Pδ .
Further, ν̄ ≤ δ in Case 1. Thus, we may massage the equation to give
p
δ ≤ c1 ν̄ log Pδ

where c1 = c0 /(1 − 2c0 ) and we take c0 < 1/2.
We now check that δ satisfies Inequality (10.3) as required for Fano’s inequality.
Since δ ≥ ν̄, one has
δ2
δ2
+
1
≤
2
≤ 2c21 log Pδ .
ν̄ 2
ν̄ 2
The inequality now follows if we take c0 such that 2c21 < c.
We now move to the second case.
Case 2: δ ≤ ν̄. This is an unusual case; it only occurs when K is quite small, e.g.,
a 1-dimensional subspace. Fano’s inequality, which is more applicable for higher
dimensional sets, may not give the optimal answer, but we may use much simpler
methods. We give a bare-bones argument.
Consider any two signals, w, v ∈ K satisfying kw − vk2 ≤ cν̄. We will show that
these two points are indistinguishable based on the noisy observations y. Consider
the hypothesis testing problem of determining which of the two points generate y,
i.e., either y = Aw + ε√or y = Av + ε. As seen from (10.2), with probability at least
3/4, kA(v − w)k2 ≤ c mν̄ ≤ cν. Thus, the difference between the two candidate
signals is much smaller than the noise level. It follows from a quick analysis of

32

YANIV PLAN, ROMAN VERSHYNIN, AND ELENA YUDOVINA

the likelihood ratio test, which is optimal by Neyman-Pearson lemma, that the that
hypotheses are indistinguishable, i.e., there is no estimator which has a more than
3/4 chance of determining the original signal. This gives the lower bound
sup E kb
x − xk2 ≥ c kv − wk2 .

x∈K

By taking the maximum distance between signals v, w ∈ K ∩ ν̄B2n , this gives
sup E kb
x − xk2 ≥ c min(diam(K), ν̄) ≥ c min(diam(K), δ).

x∈K


10.2. Proof of Corollary 4.4 incorporating Remark 4.5. We prove a generalization of Corollary 4.4 that takes the tighter version of α from Remark 4.5.
Corollary 10.3. Assume that x ∈ K where K is a star-shaped subset of Rn . Assume
that the observations yi are given by 4.1. Let

ν h
wt (K) i
δ∗ := inf t + √ 1 +
.
(10.5)
t
t
m
Then any estimator x
b satisfies

sup E kb
x − xk2 ≥ cα min(δ∗ , diam(K)),

x∈K

where cα = c/α for a numerical constant c and α defined in (4.5).
Proof. The proof proceeds simply by showing that δ2∗ = 21 δ∗ ≤ αδ∗ , with δ∗ defined
in (4.2). One may then apply Theorem 4.2. First, observe that by definition


wδ2∗ (K) i
1 ∗
ν h
∗
δ2 ≤
.
δ + √ 1+
2 2
m
δ2∗

Massage the equation to give

wδ∗ (K) i
ν h
.
δ2∗ ≤ √ 1 + 2 ∗
δ2
m

The Geometric Assumption then implies that
q
q
i
i
ν h
ν h
δ2∗ ≤ √ 1 + α log Pδ2∗ ≤ α · √ 1 + log Pδ2∗ .
m
m

Note that since K is star-shaped, Pt is decreasing in t and thus,
i
i
q
q
ν h
ν h
δ2∗ /α ≤ inf∗ √ 1 + log Pδ2∗ ≤ inf∗ {t + √ 1 + log Pδ2∗ }.
t≤δ2
t≤δ2
m
m

Now, if δ2∗ < δ∗ , then the proof is done, so we may restrict to the case δ2∗ ≥ δ∗ .
However, in this case, by definition of δ∗ , the restriction t ≤ δ2∗ may be removed
from the infimum just above, and so
i
q
ν h
δ2∗ /α ≤ inf {t + √ 1 + log Pδ2∗ } = δ∗ .
t
m


HIGH-DIMENSIONAL ESTIMATION WITH GEOMETRIC CONSTRAINTS

33

10.3. Proof of Lemma 4.9.
Proof of Lemma 4.9. The proof will follow from several manipulations and decompositions of Gaussian random variables. By definition, in the nonlinear model
µ = E f (ha1 , xi + ε1 ) ha1 , x̄i = E f (kxk2 · g + νz)g

where g, z ∼ N (0, 1) are independent. Let β 2 = Var(kxk2 · g + νz) = kxk22 + η 2 and
let
kxk2 · g + νz
∼ N (0, 1).
w :=
β
Further, decompose g as g = (kxk2 /β)w + w⊥ where w⊥ is independent of w. Thus,
µβ/ kxk2 = E f (βw)w = E [f (β|w|) · |w|] .

The second equality follows since f is assumed to be odd. Now, since f is nondecreasing, we have
E f (β|w|) · |w| ≥ f (β) · P {|w| ≥ 1} = Cf (β),

where C = P {|w| ≥ 1} ≈ 0.683.
Putting pieces together, we have

kxk2 f (β)
.
β
We now give an upper bound for σ and η.
µ≥C

σ 2 = Var(f (βw) · g) ≤ E(f (βw) · g)2 .

By Cauchy-Schwarz inequality, the right-hand side is bounded by
p
p
p
E f 4 (βw) · E g 4 = 31/2 E f 4 (βw).

(10.6)

The sub-multiplicative assumption implies that

E f 4 (βw) ≤ f 4 (β) E f 4 (w) = Cf · f 4 (β).

Thus, σ ≤ Cf f (β). As a bonus, we bounded η by the same quantity, since η 2 is
bounded by the right-hand side of (10.6). Thus,
λ(σ + η) =
where

kxk2
kxk2 f (β)β
Cf f (β) ≤ Cf (kxk2 + η),
(σ + η) ≤
µ
kxk2

Cf = C E[f 4 (w)]1/4 ,

C = 481/4 P {|w| ≥ 1} ≈ 1.8.



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36

YANIV PLAN, ROMAN VERSHYNIN, AND ELENA YUDOVINA

Y. Plan and R. Vershynin, University of Michigan Department of Mathematics,
2074 East Hall, 530 Church Street, Ann Arbor, MI 48109
E. Yudovina, University of Michigan Department of Statistics, 439 West Hall, 1085
S. University Ave., Ann Arbor, MI 48109
E-mail address: {plan, romanv, yudovina}@umich.edu

