pwc-archive/papers-with-abstracts huggingface dataset:
Sparse Factor Analysis for Learning and Content Analytics
Authors: Andrew S. Lan, Andrew E. Waters, Christoph Studer, Richard G. Baraniuk
Date: 2013-03-22 00:00:00
Tasks: Dictionary Learning

We develop a new model and algorithms for machine learning-based learning
analytics, which estimate a learner's knowledge of the concepts underlying a
domain, and content analytics, which estimate the relationships among a
collection of questions and those concepts. Our model represents the
probability that a learner provides the correct response to a question in terms
of three factors: their understanding of a set of underlying concepts, the
concepts involved in each question, and each question's intrinsic difficulty.
We estimate these factors given the graded responses to a collection of
questions. The underlying estimation problem is ill-posed in general,
especially when only a subset of the questions are answered. The key
observation that enables a well-posed solution is the fact that typical
educational domains of interest involve only a small number of key concepts.
Leveraging this observation, we develop both a bi-convex maximum-likelihood and
a Bayesian solution to the resulting SPARse Factor Analysis (SPARFA) problem.
We also incorporate user-defined tags on questions to facilitate the
interpretability of the estimated factors. Experiments with synthetic and
real-world data demonstrate the efficacy of our approach. Finally, we make a
connection between SPARFA and noisy, binary-valued (1-bit) dictionary learning
that is of independent interest.
Ontology alignment repair through modularization and confidence-based heuristics
Authors: Emanuel Santos, Daniel Faria, Cátia Pesquita, Francisco Couto
Date: 2013-07-19 00:00:00
Tasks: Ontology Matching

Ontology Matching aims to find a set of semantic correspondences, called an
alignment, between related ontologies. In recent years, there has been a
growing interest in efficient and effective matching methods for large
ontologies. However, most of the alignments produced for large ontologies are
logically incoherent. It was only recently that the use of repair techniques to
improve the quality of ontology alignments has been explored. In this paper we
present a novel technique for detecting incoherent concepts based on ontology
modularization, and a new repair algorithm that minimizes the incoherence of
the resulting alignment and the number of matches removed from the input
alignment. An implementation was done as part of a lightweight version of
AgreementMaker system, a successful ontology matching platform, and evaluated
using a set of four benchmark biomedical ontology matching tasks. Our results
show that our implementation is efficient and produces better alignments with
respect to their coherence and f-measure than the state of the art repairing
tools. They also show that our implementation is a better alternative for
producing coherent silver standard alignments.
Making Laplacians commute
Authors: Michael M. Bronstein, Klaus Glashoff, Terry A. Loring
Date: 2013-07-19 00:00:00
Tasks: Clustering, Dimensionality Reduction

In this paper, we construct multimodal spectral geometry by finding a pair of
closest commuting operators (CCO) to a given pair of Laplacians. The CCOs are
jointly diagonalizable and hence have the same eigenbasis. Our construction
naturally extends classical data analysis tools based on spectral geometry,
such as diffusion maps and spectral clustering. We provide several synthetic
and real examples of applications in dimensionality reduction, shape analysis,
and clustering, demonstrating that our method better captures the inherent
structure of multi-modal data.
Speaker Independent Continuous Speech to Text Converter for Mobile Application
Authors: R. Sandanalakshmi, P. Abinaya Viji, M. Kiruthiga, M. Manjari, M. Sharina
Date: 2013-07-19 00:00:00
Tasks: Action Detection, Activity Detection, Speech-to-Text

An efficient speech to text converter for mobile application is presented in
this work. The prime motive is to formulate a system which would give optimum
performance in terms of complexity, accuracy, delay and memory requirements for
mobile environment. The speech to text converter consists of two stages namely
front-end analysis and pattern recognition. The front end analysis involves
preprocessing and feature extraction. The traditional voice activity detection
algorithms which track only energy cannot successfully identify potential
speech from input because the unwanted part of the speech also has some energy
and appears to be speech. In the proposed system, VAD that calculates energy of
high frequency part separately as zero crossing rate to differentiate noise
from speech is used. Mel Frequency Cepstral Coefficient (MFCC) is used as
feature extraction method and Generalized Regression Neural Network is used as
recognizer. MFCC provides low word error rate and better feature extraction.
Neural Network improves the accuracy. Thus a small database containing all
possible syllable pronunciation of the user is sufficient to give recognition
accuracy closer to 100%. Thus the proposed technique entertains realization of
real time speaker independent applications like mobile phones, PDAs etc.
Model-Based Policy Gradients with Parameter-Based Exploration by Least-Squares Conditional Density Estimation
Authors: Syogo Mori, Voot Tangkaratt, Tingting Zhao, Jun Morimoto, Masashi Sugiyama
Date: 2013-07-19 00:00:00
Tasks: Density Estimation, Reinforcement Learning, Reinforcement Learning (RL)

The goal of reinforcement learning (RL) is to let an agent learn an optimal
control policy in an unknown environment so that future expected rewards are
maximized. The model-free RL approach directly learns the policy based on data
samples. Although using many samples tends to improve the accuracy of policy
learning, collecting a large number of samples is often expensive in practice.
On the other hand, the model-based RL approach first estimates the transition
model of the environment and then learns the policy based on the estimated
transition model. Thus, if the transition model is accurately learned from a
small amount of data, the model-based approach can perform better than the
model-free approach. In this paper, we propose a novel model-based RL method by
combining a recently proposed model-free policy search method called policy
gradients with parameter-based exploration and the state-of-the-art transition
model estimator called least-squares conditional density estimation. Through
experiments, we demonstrate the practical usefulness of the proposed method.
Automated Defect Localization via Low Rank Plus Outlier Modeling of Propagating Wavefield Data
Authors: Stefano Gonella, Jarvis D. Haupt
Date: 2013-07-19 00:00:00

This work proposes an agnostic inference strategy for material diagnostics,
conceived within the context of laser-based non-destructive evaluation methods,
which extract information about structural anomalies from the analysis of
acoustic wavefields measured on the structure's surface by means of a scanning
laser interferometer. The proposed approach couples spatiotemporal windowing
with low rank plus outlier modeling, to identify a priori unknown deviations in
the propagating wavefields caused by material inhomogeneities or defects, using
virtually no knowledge of the structural and material properties of the medium.
This characteristic makes the approach particularly suitable for diagnostics
scenarios where the mechanical and material models are complex, unknown, or
unreliable. We demonstrate our approach in a simulated environment using
benchmark point and line defect localization problems based on propagating
flexural waves in a thin plate.
A Safe Screening Rule for Sparse Logistic Regression
Authors: Jie Wang, Jiayu Zhou, Jun Liu, Peter Wonka, Jieping Ye
Date: 2013-07-16 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: feature selection, regression

The l1-regularized logistic regression (or sparse logistic regression) is a
widely used method for simultaneous classification and feature selection.
Although many recent efforts have been devoted to its efficient implementation,
its application to high dimensional data still poses significant challenges. In
this paper, we present a fast and effective sparse logistic regression
screening rule (Slores) to identify the 0 components in the solution vector,
which may lead to a substantial reduction in the number of features to be
entered to the optimization. An appealing feature of Slores is that the data
set needs to be scanned only once to run the screening and its computational
cost is negligible compared to that of solving the sparse logistic regression
problem. Moreover, Slores is independent of solvers for sparse logistic
regression, thus Slores can be integrated with any existing solver to improve
the efficiency. We have evaluated Slores using high-dimensional data sets from
different applications. Extensive experimental results demonstrate that Slores
outperforms the existing state-of-the-art screening rules and the efficiency of
solving sparse logistic regression is improved by one magnitude in general.
Nonlinear unmixing of hyperspectral images: models and algorithms
Authors: Nicolas Dobigeon, Jean-Yves Tourneret, Cédric Richard, José C. M. Bermudez, Stephen McLaughlin, Alfred O. Hero
Date: 2013-04-06 00:00:00
Tasks: valid

When considering the problem of unmixing hyperspectral images, most of the
literature in the geoscience and image processing areas relies on the widely
used linear mixing model (LMM). However, the LMM may be not valid and other
nonlinear models need to be considered, for instance, when there are
multi-scattering effects or intimate interactions. Consequently, over the last
few years, several significant contributions have been proposed to overcome the
limitations inherent in the LMM. In this paper, we present an overview of
recent advances in nonlinear unmixing modeling.
Noisy Subspace Clustering via Thresholding
Authors: Reinhard Heckel, Helmut Bölcskei
Date: 2013-05-15 00:00:00
Tasks: Clustering, Outlier Detection

We consider the problem of clustering noisy high-dimensional data points into
a union of low-dimensional subspaces and a set of outliers. The number of
subspaces, their dimensions, and their orientations are unknown. A
probabilistic performance analysis of the thresholding-based subspace
clustering (TSC) algorithm introduced recently in [1] shows that TSC succeeds
in the noisy case, even when the subspaces intersect. Our results reveal an
explicit tradeoff between the allowed noise level and the affinity of the
subspaces. We furthermore find that the simple outlier detection scheme
introduced in [1] provably succeeds in the noisy case.
Variational Algorithms for Marginal MAP
Authors: Qiang Liu, Alexander Ihler
Date: 2013-02-26 00:00:00

The marginal maximum a posteriori probability (MAP) estimation problem, which
calculates the mode of the marginal posterior distribution of a subset of
variables with the remaining variables marginalized, is an important inference
problem in many models, such as those with hidden variables or uncertain
parameters. Unfortunately, marginal MAP can be NP-hard even on trees, and has
attracted less attention in the literature compared to the joint MAP
(maximization) and marginalization problems. We derive a general dual
representation for marginal MAP that naturally integrates the marginalization
and maximization operations into a joint variational optimization problem,
making it possible to easily extend most or all variational-based algorithms to
marginal MAP. In particular, we derive a set of "mixed-product" message passing
algorithms for marginal MAP, whose form is a hybrid of max-product, sum-product
and a novel "argmax-product" message updates. We also derive a class of
convergent algorithms based on proximal point methods, including one that
transforms the marginal MAP problem into a sequence of standard marginalization
problems. Theoretically, we provide guarantees under which our algorithms give
globally or locally optimal solutions, and provide novel upper bounds on the
optimal objectives. Empirically, we demonstrate that our algorithms
significantly outperform the existing approaches, including a state-of-the-art
algorithm based on local search methods.
Regret Bounds for Reinforcement Learning with Policy Advice
Authors: Mohammad Gheshlaghi Azar, Alessandro Lazaric, Emma Brunskill
Date: 2013-05-05 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In some reinforcement learning problems an agent may be provided with a set
of input policies, perhaps learned from prior experience or provided by
advisors. We present a reinforcement learning with policy advice (RLPA)
algorithm which leverages this input set and learns to use the best policy in
the set for the reinforcement learning task at hand. We prove that RLPA has a
sub-linear regret of \tilde O(\sqrt{T}) relative to the best input policy, and
that both this regret and its computational complexity are independent of the
size of the state and action space. Our empirical simulations support our
theoretical analysis. This suggests RLPA may offer significant advantages in
large domains where some prior good policies are provided.
Approximation Algorithms for Bayesian Multi-Armed Bandit Problems
Authors: Sudipto Guha, Kamesh Munagala
Date: 2013-06-14 00:00:00

In this paper, we consider several finite-horizon Bayesian multi-armed bandit
problems with side constraints which are computationally intractable (NP-Hard)
and for which no optimal (or near optimal) algorithms are known to exist with
sub-exponential running time. All of these problems violate the standard
exchange property, which assumes that the reward from the play of an arm is not
contingent upon when the arm is played. Not only are index policies suboptimal
in these contexts, there has been little analysis of such policies in these
problem settings. We show that if we consider near-optimal policies, in the
sense of approximation algorithms, then there exists (near) index policies.
Conceptually, if we can find policies that satisfy an approximate version of
the exchange property, namely, that the reward from the play of an arm depends
on when the arm is played to within a constant factor, then we have an avenue
towards solving these problems. However such an approximate version of the
idling bandit property does not hold on a per-play basis and are shown to hold
in a global sense. Clearly, such a property is not necessarily true of
arbitrary single arm policies and finding such single arm policies is
nontrivial. We show that by restricting the state spaces of arms we can find
single arm policies and that these single arm policies can be combined into
global (near) index policies where the approximate version of the exchange
property is true in expectation. The number of different bandit problems that
can be addressed by this technique already demonstrate its wide applicability.
Content Based Image Retrieval System using Feature Classification with Modified KNN Algorithm
Authors: T. Dharani, I. Laurence Aroquiaraj
Date: 2013-07-17 00:00:00
Tasks: Content-Based Image Retrieval, General Classification, Image Retrieval, Retrieval

Feature means countenance, remote sensing scene objects with similar
characteristics, associated to interesting scene elements in the image
formation process. They are classified into three types in image processing,
that is low, middle and high. Low level features are color, texture and middle
level feature is shape and high level feature is semantic gap of objects. An
image retrieval system is a computer system for browsing, searching and
retrieving images from a large image database. Content Based Image Retrieval is
a technique which uses visual features of image such as color, shape, texture
to search user required image from large image database according to user
requests in the form of a query. MKNN is an enhancing method of KNN. The
proposed KNN classification is called MKNN. MKNN contains two parts for
processing, they are validity of the train samples and applying weighted KNN.
The validity of each point is computed according to its neighbors. In our
proposal, Modified K-Nearest Neighbor can be considered a kind of weighted KNN
so that the query label is approximated by weighting the neighbors of the
query.
DASH: Dynamic Approach for Switching Heuristics
Authors: Giovanni Di Liberto, Serdar Kadioglu, Kevin Leo, Yuri Malitsky
Date: 2013-07-17 00:00:00

Complete tree search is a highly effective method for tackling MIP problems,
and over the years, a plethora of branching heuristics have been introduced to
further refine the technique for varying problems. Recently, portfolio
algorithms have taken the process a step further, trying to predict the best
heuristic for each instance at hand. However, the motivation behind algorithm
selection can be taken further still, and used to dynamically choose the most
appropriate algorithm for each encountered subproblem. In this paper we
identify a feature space that captures both the evolution of the problem in the
branching tree and the similarity among subproblems of instances from the same
MIP models. We show how to exploit these features to decide the best time to
switch the branching heuristic and then show how such a system can be trained
efficiently. Experiments on a highly heterogeneous collection of MIP instances
show significant gains over the pure algorithm selection approach that for a
given instance uses only a single heuristic throughout the search.
The connection between Bayesian estimation of a Gaussian random field and RKHS
Authors: Aleksandr Y. Aravkin, Bradley M. Bell, James V. Burke, Gianluigi Pillonetto
Date: 2013-01-22 00:00:00

Reconstruction of a function from noisy data is often formulated as a
regularized optimization problem over an infinite-dimensional reproducing
kernel Hilbert space (RKHS). The solution describes the observed data and has a
small RKHS norm. When the data fit is measured using a quadratic loss, this
estimator has a known statistical interpretation. Given the noisy measurements,
the RKHS estimate represents the posterior mean (minimum variance estimate) of
a Gaussian random field with covariance proportional to the kernel associated
with the RKHS. In this paper, we provide a statistical interpretation when more
general losses are used, such as absolute value, Vapnik or Huber. Specifically,
for any finite set of sampling locations (including where the data were
collected), the MAP estimate for the signal samples is given by the RKHS
estimate evaluated at these locations.
A New Convex Relaxation for Tensor Completion
Authors: Bernardino Romera-Paredes, Massimiliano Pontil
Date: 2013-07-17 00:00:00
Proceeding: NeurIPS 2013 12

We study the problem of learning a tensor from a set of linear measurements.
A prominent methodology for this problem is based on a generalization of trace
norm regularization, which has been used extensively for learning low rank
matrices, to the tensor setting. In this paper, we highlight some limitations
of this approach and propose an alternative convex relaxation on the Euclidean
ball. We then describe a technique to solve the associated regularization
problem, which builds upon the alternating direction method of multipliers.
Experiments on one synthetic dataset and two real datasets indicate that the
proposed method improves significantly over tensor trace norm regularization in
terms of estimation error, while remaining computationally tractable.
Convex Variational Image Restoration with Histogram Priors
Authors: Paul Swoboda, Christoph Schnörr
Date: 2013-01-16 00:00:00
Tasks: Denoising, Image Restoration

We present a novel variational approach to image restoration (e.g.,
denoising, inpainting, labeling) that enables to complement established
variational approaches with a histogram-based prior enforcing closeness of the
solution to some given empirical measure. By minimizing a single objective
function, the approach utilizes simultaneously two quite different sources of
information for restoration: spatial context in terms of some smoothness prior
and non-spatial statistics in terms of the novel prior utilizing the
Wasserstein distance between probability measures. We study the combination of
the functional lifting technique with two different relaxations of the
histogram prior and derive a jointly convex variational approach. Mathematical
equivalence of both relaxations is established and cases where optimality holds
are discussed. Additionally, we present an efficient algorithmic scheme for the
numerical treatment of the presented model. Experiments using the basic
total-variation based denoising approach as a case study demonstrate our novel
regularization approach.
Symmetries in LDDMM with higher order momentum distributions
Authors: Henry Jacobs
Date: 2013-06-14 00:00:00
Tasks: Image Registration

In some implementations of the Large Deformation Diffeomorphic Metric Mapping
formulation for image registration we consider the motion of particles which
locally translate image data. We then lift the motion of the particles to
obtain a motion on the entire image. However, it is certainly possible to
consider particles which do more than translate, and this is what will be
described in this paper. As the unreduced Lagrangian associated to EPDiff
possesses $\Diff(M)$ symmetry, it must also exhibit $G \subset \Diff(M)$
symmetry, for any Lie subgroup. In this paper we will describe a tower of Lie
groups $G^{(0)} \subseteq G^{(1)} \subseteq G^{(2)} \subseteq...$ which
correspond to preserving $k$-th order jet-data. The reduced configuration
spaces $Q^{(k)} := \Diff(M) / G^{(k)}$ will be finite-dimensional (in
particular, $Q^{(0)}$ is the configuration manifold for $N$ particles in $M$).
We will observe that $G^{(k)}$ is a normal subgroup of $G^{(0)}$ and so the
quotient $G^{(0)} / G^{(k)}$ is itself a (finite dimensional) Lie group which
acts on $Q^{(k)}$. This makes $Q^{(k)}$ a principle bundle over $Q^{(0)}$ and
the reduced geodesic equations on $Q^{(k)}$ will possess $G^{(0)} /
G^{(k)}$-symmetry. Noether's theorem implies the existence of conserved momenta
for the reduced system on $T^{\ast}Q^{(k)}$.
Processing stationary noise: model and parameter selection in variational methods
Authors: Jérôme Fehrenbach, Pierre Weiss
Date: 2013-07-17 00:00:00
Tasks: Denoising

Additive or multiplicative stationary noise recently became an important
issue in applied fields such as microscopy or satellite imaging. Relatively few
works address the design of dedicated denoising methods compared to the usual
white noise setting. We recently proposed a variational algorithm to tackle
this issue. In this paper, we analyze this problem from a statistical point of
view and provide deterministic properties of the solutions of the associated
variational problems. In the first part of this work, we demonstrate that in
many practical problems, the noise can be assimilated to a colored Gaussian
noise. We provide a quantitative measure of the distance between a stationary
process and the corresponding Gaussian process. In the second part, we focus on
the Gaussian setting and analyze denoising methods which consist of minimizing
the sum of a total variation term and an $l^2$ data fidelity term. While the
constrained formulation of this problem allows to easily tune the parameters,
the Lagrangian formulation can be solved more efficiently since the problem is
strongly convex. Our second contribution consists in providing analytical
values of the regularization parameter in order to approximately satisfy
Morozov's discrepancy principle.