pwc-archive/papers-with-abstracts huggingface dataset:
Meteorological time series forecasting based on MLP modelling using heterogeneous transfer functions
Authors: Cyril Voyant, Marie Laure Nivet, Christophe Paoli, Marc Muselli, Gilles Notton
Date: 2014-04-29 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Forecasting

In this paper, we propose to study four meteorological and seasonal time
series coupled with a multi-layer perceptron (MLP) modeling. We chose to
combine two transfer functions for the nodes of the hidden layer, and to use a
temporal indicator (time index as input) in order to take into account the
seasonal aspect of the studied time series. The results of the prediction
concern two years of measurements and the learning step, eight independent
years. We show that this methodology can improve the accuracy of meteorological
data estimation compared to a classical MLP modelling with a homogenous
transfer function.
High Dimensional Semiparametric Latent Graphical Model for Mixed Data
Authors: Jianqing Fan, Han Liu, Yang Ning, Hui Zou
Date: 2014-04-29 00:00:00
Tasks: feature selection, Vocal Bursts Intensity Prediction

Graphical models are commonly used tools for modeling multivariate random
variables. While there exist many convenient multivariate distributions such as
Gaussian distribution for continuous data, mixed data with the presence of
discrete variables or a combination of both continuous and discrete variables
poses new challenges in statistical modeling. In this paper, we propose a
semiparametric model named latent Gaussian copula model for binary and mixed
data. The observed binary data are assumed to be obtained by dichotomizing a
latent variable satisfying the Gaussian copula distribution or the
nonparanormal distribution. The latent Gaussian model with the assumption that
the latent variables are multivariate Gaussian is a special case of the
proposed model. A novel rank-based approach is proposed for both latent graph
estimation and latent principal component analysis. Theoretically, the proposed
methods achieve the same rates of convergence for both precision matrix
estimation and eigenvector estimation, as if the latent variables were
observed. Under similar conditions, the consistency of graph structure recovery
and feature selection for leading eigenvectors is established. The performance
of the proposed methods is numerically assessed through simulation studies, and
the usage of our methods is illustrated by a genetic dataset.
Structural Group Sparse Representation for Image Compressive Sensing Recovery
Authors: Jian Zhang, Debin Zhao, Feng Jiang, Wen Gao
Date: 2014-04-29 00:00:00
Tasks: Compressive Sensing

Compressive Sensing (CS) theory shows that a signal can be decoded from many
fewer measurements than suggested by the Nyquist sampling theory, when the
signal is sparse in some domain. Most of conventional CS recovery approaches,
however, exploited a set of fixed bases (e.g. DCT, wavelet, contourlet and
gradient domain) for the entirety of a signal, which are irrespective of the
nonstationarity of natural signals and cannot achieve high enough degree of
sparsity, thus resulting in poor rate-distortion performance. In this paper, we
propose a new framework for image compressive sensing recovery via structural
group sparse representation (SGSR) modeling, which enforces image sparsity and
self-similarity simultaneously under a unified framework in an adaptive group
domain, thus greatly confining the CS solution space. In addition, an efficient
iterative shrinkage/thresholding algorithm based technique is developed to
solve the above optimization problem. Experimental results demonstrate that the
novel CS recovery strategy achieves significant performance improvements over
the current state-of-the-art schemes and exhibits nice convergence.
Spatially Directional Predictive Coding for Block-based Compressive Sensing of Natural Images
Authors: Jian Zhang, Debin Zhao, Feng Jiang
Date: 2014-04-29 00:00:00
Tasks: Compressive Sensing, Decoder

A novel coding strategy for block-based compressive sens-ing named spatially
directional predictive coding (SDPC) is proposed, which efficiently utilizes
the intrinsic spatial cor-relation of natural images. At the encoder, for each
block of compressive sensing (CS) measurements, the optimal pre-diction is
selected from a set of prediction candidates that are generated by four
designed directional predictive modes. Then, the resulting residual is
processed by scalar quantiza-tion (SQ). At the decoder, the same prediction is
added onto the de-quantized residuals to produce the quantized CS measurements,
which is exploited for CS reconstruction. Experimental results substantiate
significant improvements achieved by SDPC-plus-SQ in rate distortion
performance as compared with SQ alone and DPCM-plus-SQ.
Generalizing Modular Logic Programs
Authors: João Moura, Carlos Damásio
Date: 2014-04-29 00:00:00

Even though modularity has been studied extensively in conventional logic
programming, there are few approaches on how to incorporate modularity into
Answer Set Programming, a prominent rule-based declarative programming
paradigm. A major approach is Oikarinnen and Janhunen's Gaifman-Shapiro-style
architecture of program modules, which provides the composition of program
modules. Their module theorem properly strengthens Lifschitz and Turner's
splitting set theorem for normal logic programs. However, this approach is
limited by module conditions that are imposed in order to ensure the
compatibility of their module system with the stable model semantics, namely
forcing output signatures of composing modules to be disjoint and disallowing
positive cyclic dependencies between different modules. These conditions turn
out to be too restrictive in practice and in this paper we discuss alternative
ways of lift both restrictions independently, effectively solving the first,
widening the applicability of this framework and the scope of the module
theorem.
Randomized Sketches of Convex Programs with Sharp Guarantees
Authors: Mert Pilanci, Martin J. Wainwright
Date: 2014-04-29 00:00:00
Tasks: compressed sensing, Dimensionality Reduction

Random projection (RP) is a classical technique for reducing storage and
computational costs. We analyze RP-based approximations of convex programs, in
which the original optimization problem is approximated by the solution of a
lower-dimensional problem. Such dimensionality reduction is essential in
computation-limited settings, since the complexity of general convex
programming can be quite high (e.g., cubic for quadratic programs, and
substantially higher for semidefinite programs). In addition to computational
savings, random projection is also useful for reducing memory usage, and has
useful properties for privacy-sensitive optimization. We prove that the
approximation ratio of this procedure can be bounded in terms of the geometry
of constraint set. For a broad class of random projections, including those
based on various sub-Gaussian distributions as well as randomized Hadamard and
Fourier transforms, the data matrix defining the cost function can be projected
down to the statistical dimension of the tangent cone of the constraints at the
original solution, which is often substantially smaller than the original
dimension. We illustrate consequences of our theory for various cases,
including unconstrained and $\ell_1$-constrained least squares, support vector
machines, low-rank matrix estimation, and discuss implications on
privacy-sensitive optimization and some connections with de-noising and
compressed sensing.
Fast Approximation of Rotations and Hessians matrices
Authors: Michael Mathieu, Yann Lecun
Date: 2014-04-29 00:00:00

A new method to represent and approximate rotation matrices is introduced.
The method represents approximations of a rotation matrix $Q$ with linearithmic
complexity, i.e. with $\frac{1}{2}n\lg(n)$ rotations over pairs of coordinates,
arranged in an FFT-like fashion. The approximation is "learned" using gradient
descent. It allows to represent symmetric matrices $H$ as $QDQ^T$ where $D$ is
a diagonal matrix. It can be used to approximate covariance matrix of Gaussian
models in order to speed up inference, or to estimate and track the inverse
Hessian of an objective function by relating changes in parameters to changes
in gradient along the trajectory followed by the optimization procedure.
Experiments were conducted to approximate synthetic matrices, covariance
matrices of real data, and Hessian matrices of objective functions involved in
machine learning problems.
Nonmonotonic Reasoning as a Temporal Activity
Authors: Daniel G. Schwartz
Date: 2014-04-28 00:00:00

A {\it dynamic reasoning system} (DRS) is an adaptation of a conventional
formal logical system that explicitly portrays reasoning as a temporal
activity, with each extralogical input to the system and each inference rule
application being viewed as occurring at a distinct time step. Every DRS
incorporates some well-defined logic together with a controller that serves to
guide the reasoning process in response to user inputs. Logics are generic,
whereas controllers are application-specific. Every controller does,
nonetheless, provide an algorithm for nonmonotonic belief revision. The general
notion of a DRS comprises a framework within which one can formulate the logic
and algorithms for a given application and prove that the algorithms are
correct, i.e., that they serve to (i) derive all salient information and (ii)
preserve the consistency of the belief set. This paper illustrates the idea
with ordinary first-order predicate calculus, suitably modified for the present
purpose, and an example. The example revisits some classic nonmonotonic
reasoning puzzles (Opus the Penguin, Nixon Diamond) and shows how these can be
resolved in the context of a DRS, using an expanded version of first-order
logic that incorporates typed predicate symbols. All concepts are rigorously
defined and effectively computable, thereby providing the foundation for a
future software implementation.
Kernel-based Distance Metric Learning in the Output Space
Authors: Cong Li, Michael Georgiopoulos, Georgios C. Anagnostopoulos
Date: 2013-12-09 00:00:00
Tasks: General Classification, Metric Learning

In this paper we present two related, kernel-based Distance Metric Learning
(DML) methods. Their respective models non-linearly map data from their
original space to an output space, and subsequent distance measurements are
performed in the output space via a Mahalanobis metric. The dimensionality of
the output space can be directly controlled to facilitate the learning of a
low-rank metric. Both methods allow for simultaneous inference of the
associated metric and the mapping to the output space, which can be used to
visualize the data, when the output space is 2- or 3-dimensional. Experimental
results for a collection of classification tasks illustrate the advantages of
the proposed methods over other traditional and kernel-based DML approaches.
Automatic Differentiation of Algorithms for Machine Learning
Authors: Atilim Gunes Baydin, Barak A. Pearlmutter
Date: 2014-04-28 00:00:00
Tasks: BIG-bench Machine Learning

Automatic differentiation---the mechanical transformation of numeric computer
programs to calculate derivatives efficiently and accurately---dates to the
origin of the computer age. Reverse mode automatic differentiation both
antedates and generalizes the method of backwards propagation of errors used in
machine learning. Despite this, practitioners in a variety of fields, including
machine learning, have been little influenced by automatic differentiation, and
make scant use of available tools. Here we review the technique of automatic
differentiation, describe its two main modes, and explain how it can benefit
machine learning practitioners. To reach the widest possible audience our
treatment assumes only elementary differential calculus, and does not assume
any knowledge of linear algebra.
Preliminary Report on WASP 2.0
Authors: Mario Alviano, Carmine Dodaro, Francesco Ricca
Date: 2014-04-28 00:00:00

Answer Set Programming (ASP) is a declarative programming paradigm. The
intrinsic complexity of the evaluation of ASP programs makes the development of
more effective and faster systems a challenging research topic. This paper
reports on the recent improvements of the ASP solver WASP. WASP is undergoing a
refactoring process which will end up in the release of a new and more
performant version of the software. In particular the paper focus on the
improvements to the core evaluation algorithms working on normal programs. A
preliminary experiment on benchmarks from the 3rd ASP competition belonging to
the NP class is reported. The previous version of WASP was often not
competitive with alternative solutions on this class. The new version of WASP
shows a substantial increase in performance.
Poisson noise reduction with non-local PCA
Authors: Joseph Salmon, Zachary Harmany, Charles-Alban Deledalle, Rebecca Willett
Date: 2012-06-02 00:00:00
Tasks: Astronomy, Denoising, Dictionary Learning

Photon-limited imaging arises when the number of photons collected by a
sensor array is small relative to the number of detector elements. Photon
limitations are an important concern for many applications such as spectral
imaging, night vision, nuclear medicine, and astronomy. Typically a Poisson
distribution is used to model these observations, and the inherent
heteroscedasticity of the data combined with standard noise removal methods
yields significant artifacts. This paper introduces a novel denoising algorithm
for photon-limited images which combines elements of dictionary learning and
sparse patch-based representations of images. The method employs both an
adaptation of Principal Component Analysis (PCA) for Poisson noise and recently
developed sparsity-regularized convex optimization algorithms for
photon-limited images. A comprehensive empirical evaluation of the proposed
method helps characterize the performance of this approach relative to other
state-of-the-art denoising methods. The results reveal that, despite its
conceptual simplicity, Poisson PCA-based denoising appears to be highly
competitive in very low light regimes.
Credulous and Skeptical Argument Games for Complete Semantics in Conflict Resolution based Argumentation
Authors: Jozef Frtús
Date: 2014-04-28 00:00:00

Argumentation is one of the most popular approaches of defining
a~non-monotonic formalism and several argumentation based semantics were
proposed for defeasible logic programs. Recently, a new approach based on
notions of conflict resolutions was proposed, however with declarative
semantics only. This paper gives a more procedural counterpart by developing
skeptical and credulous argument games for complete semantics and soundness and
completeness theorems for both games are provided. After that, distribution of
defeasible logic program into several contexts is investigated and both
argument games are adapted for multi-context system.
Conditional Density Estimation with Dimensionality Reduction via Squared-Loss Conditional Entropy Minimization
Authors: Voot Tangkaratt, Ning Xie, Masashi Sugiyama
Date: 2014-04-28 00:00:00
Tasks: Density Estimation, Dimensionality Reduction, regression

Regression aims at estimating the conditional mean of output given input.
However, regression is not informative enough if the conditional density is
multimodal, heteroscedastic, and asymmetric. In such a case, estimating the
conditional density itself is preferable, but conditional density estimation
(CDE) is challenging in high-dimensional space. A naive approach to coping with
high-dimensionality is to first perform dimensionality reduction (DR) and then
execute CDE. However, such a two-step process does not perform well in practice
because the error incurred in the first DR step can be magnified in the second
CDE step. In this paper, we propose a novel single-shot procedure that performs
CDE and DR simultaneously in an integrated way. Our key idea is to formulate DR
as the problem of minimizing a squared-loss variant of conditional entropy, and
this is solved via CDE. Thus, an additional CDE step is not needed after DR. We
demonstrate the usefulness of the proposed method through extensive experiments
on various datasets including humanoid robot transition and computer art.
Proximal Iteratively Reweighted Algorithm with Multiple Splitting for Nonconvex Sparsity Optimization
Authors: Canyi Lu, Yunchao Wei, Zhouchen Lin, Shuicheng Yan
Date: 2014-04-28 00:00:00

This paper proposes the Proximal Iteratively REweighted (PIRE) algorithm for
solving a general problem, which involves a large body of nonconvex sparse and
structured sparse related problems. Comparing with previous iterative solvers
for nonconvex sparse problem, PIRE is much more general and efficient. The
computational cost of PIRE in each iteration is usually as low as the
state-of-the-art convex solvers. We further propose the PIRE algorithm with
Parallel Splitting (PIRE-PS) and PIRE algorithm with Alternative Updating
(PIRE-AU) to handle the multi-variable problems. In theory, we prove that our
proposed methods converge and any limit solution is a stationary point.
Extensive experiments on both synthesis and real data sets demonstrate that our
methods achieve comparative learning performance, but are much more efficient,
by comparing with previous nonconvex solvers.
Learning Deep Convolutional Features for MRI Based Alzheimer's Disease Classification
Authors: Fayao Liu, Chunhua Shen
Date: 2014-04-13 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification, Hippocampus

Effective and accurate diagnosis of Alzheimer's disease (AD) or mild
cognitive impairment (MCI) can be critical for early treatment and thus has
attracted more and more attention nowadays. Since first introduced, machine
learning methods have been gaining increasing popularity for AD related
research. Among the various identified biomarkers, magnetic resonance imaging
(MRI) are widely used for the prediction of AD or MCI. However, before a
machine learning algorithm can be applied, image features need to be extracted
to represent the MRI images. While good representations can be pivotal to the
classification performance, almost all the previous studies typically rely on
human labelling to find the regions of interest (ROI) which may be correlated
to AD, such as hippocampus, amygdala, precuneus, etc. This procedure requires
domain knowledge and is costly and tedious.
  Instead of relying on extraction of ROI features, it is more promising to
remove manual ROI labelling from the pipeline and directly work on the raw MRI
images. In other words, we can let the machine learning methods to figure out
these informative and discriminative image structures for AD classification. In
this work, we propose to learn deep convolutional image features using
unsupervised and supervised learning. Deep learning has emerged as a powerful
tool in the machine learning community and has been successfully applied to
various tasks. We thus propose to exploit deep features of MRI images based on
a pre-trained large convolutional neural network (CNN) for AD and MCI
classification, which spares the effort of manual ROI annotation process.
Subspace clustering of dimensionality-reduced data
Authors: Reinhard Heckel, Michael Tschannen, Helmut Bölcskei
Date: 2014-04-27 00:00:00
Tasks: Clustering, Dimensionality Reduction

Subspace clustering refers to the problem of clustering unlabeled
high-dimensional data points into a union of low-dimensional linear subspaces,
assumed unknown. In practice one may have access to dimensionality-reduced
observations of the data only, resulting, e.g., from "undersampling" due to
complexity and speed constraints on the acquisition device. More pertinently,
even if one has access to the high-dimensional data set it is often desirable
to first project the data points into a lower-dimensional space and to perform
the clustering task there; this reduces storage requirements and computational
cost. The purpose of this paper is to quantify the impact of
dimensionality-reduction through random projection on the performance of the
sparse subspace clustering (SSC) and the thresholding based subspace clustering
(TSC) algorithms. We find that for both algorithms dimensionality reduction
down to the order of the subspace dimensions is possible without incurring
significant performance degradation. The mathematical engine behind our
theorems is a result quantifying how the affinities between subspaces change
under random dimensionality reducing projections.
Overlapping Trace Norms in Multi-View Learning
Authors: Behrouz Behmardi, Cedric Archambeau, Guillaume Bouchard
Date: 2014-04-24 00:00:00
Tasks: Imputation, MULTI-VIEW LEARNING

Multi-view learning leverages correlations between different sources of data
to make predictions in one view based on observations in another view. A
popular approach is to assume that, both, the correlations between the views
and the view-specific covariances have a low-rank structure, leading to
inter-battery factor analysis, a model closely related to canonical correlation
analysis. We propose a convex relaxation of this model using structured norm
regularization. Further, we extend the convex formulation to a robust version
by adding an l1-penalized matrix to our estimator, similarly to convex robust
PCA. We develop and compare scalable algorithms for several convex multi-view
models. We show experimentally that the view-specific correlations are
improving data imputation performances, as well as labeling accuracy in
real-world multi-label prediction tasks.
Robust and Efficient Subspace Segmentation via Least Squares Regression
Authors: Can-Yi Lu, Hai Min, Zhong-Qiu Zhao, Lin Zhu, De-Shuang Huang, Shuicheng Yan
Date: 2014-04-27 00:00:00
Tasks: regression, Segmentation

This paper studies the subspace segmentation problem which aims to segment
data drawn from a union of multiple linear subspaces. Recent works by using
sparse representation, low rank representation and their extensions attract
much attention. If the subspaces from which the data drawn are independent or
orthogonal, they are able to obtain a block diagonal affinity matrix, which
usually leads to a correct segmentation. The main differences among them are
their objective functions. We theoretically show that if the objective function
satisfies some conditions, and the data are sufficiently drawn from independent
subspaces, the obtained affinity matrix is always block diagonal. Furthermore,
the data sampling can be insufficient if the subspaces are orthogonal. Some
existing methods are all special cases. Then we present the Least Squares
Regression (LSR) method for subspace segmentation. It takes advantage of data
correlation, which is common in real data. LSR encourages a grouping effect
which tends to group highly correlated data together. Experimental results on
the Hopkins 155 database and Extended Yale Database B show that our method
significantly outperforms state-of-the-art methods. Beyond segmentation
accuracy, all experiments demonstrate that LSR is much more efficient.