pwc-archive/papers-with-abstracts huggingface dataset:
Int{é}gration d'une mesure d'ind{é}pendance pour la fusion d'informations
Authors: Mouloud Kharoune, Arnaud Martin
Date: 2015-01-22 00:00:00

Many information sources are considered into data fusion in order to improve
the decision in terms of uncertainty and imprecision. For each technique used
for data fusion, the asumption on independance is usually made. We propose in
this article an approach to take into acount an independance measure befor to
make the combination of information in the context of the theory of belief
functions.
Second-Order Belief Hidden Markov Models
Authors: Jungyeul Park, Mouna Chebbah, Siwar Jendoubi, Arnaud Martin
Date: 2015-01-22 00:00:00

Hidden Markov Models (HMMs) are learning methods for pattern recognition. The
probabilistic HMMs have been one of the most used techniques based on the
Bayesian model. First-order probabilistic HMMs were adapted to the theory of
belief functions such that Bayesian probabilities were replaced with mass
functions. In this paper, we present a second-order Hidden Markov Model using
belief functions. Previous works in belief HMMs have been focused on the
first-order HMMs. We extend them to the second-order model.
Features modeling with an $α$-stable distribution: Application to pattern recognition based on continuous belief functions
Authors: Anthony Fiche, Jean-Christophe Cexus, Arnaud Martin, Ali Khenchaf
Date: 2015-01-22 00:00:00
Tasks: General Classification, Sand

The aim of this paper is to show the interest in fitting features with an
$\alpha$-stable distribution to classify imperfect data. The supervised pattern
recognition is thus based on the theory of continuous belief functions, which
is a way to consider imprecision and uncertainty of data. The distributions of
features are supposed to be unimodal and estimated by a single Gaussian and
$\alpha$-stable model. Experimental results are first obtained from synthetic
data by combining two features of one dimension and by considering a vector of
two features. Mass functions are calculated from plausibility functions by
using the generalized Bayes theorem. The same study is applied to the automatic
classification of three types of sea floor (rock, silt and sand) with features
acquired by a mono-beam echo-sounder. We evaluate the quality of the
$\alpha$-stable model and the Gaussian model by analyzing qualitative results,
using a Kolmogorov-Smirnov test (K-S test), and quantitative results with
classification rates. The performances of the belief classifier are compared
with a Bayesian approach.
Sketch and Validate for Big Data Clustering
Authors: Panagiotis A. Traganitis, Konstantinos Slavakis, Georgios B. Giannakis
Date: 2015-01-22 00:00:00
Tasks: Clustering, Computational Efficiency

In response to the need for learning tools tuned to big data analytics, the
present paper introduces a framework for efficient clustering of huge sets of
(possibly high-dimensional) data. Building on random sampling and consensus
(RANSAC) ideas pursued earlier in a different (computer vision) context for
robust regression, a suite of novel dimensionality and set-reduction algorithms
is developed. The advocated sketch-and-validate (SkeVa) family includes two
algorithms that rely on K-means clustering per iteration on reduced number of
dimensions and/or feature vectors: The first operates in a batch fashion, while
the second sequential one offers computational efficiency and suitability with
streaming modes of operation. For clustering even nonlinearly separable
vectors, the SkeVa family offers also a member based on user-selected kernel
functions. Further trading off performance for reduced complexity, a fourth
member of the SkeVa family is based on a divergence criterion for selecting
proper minimal subsets of feature variables and vectors, thus bypassing the
need for K-means clustering per iteration. Extensive numerical tests on
synthetic and real data sets highlight the potential of the proposed
algorithms, and demonstrate their competitive performance relative to
state-of-the-art random projection alternatives.
Estimating the Intrinsic Dimension of Hyperspectral Images Using an Eigen-Gap Approach
Authors: A. Halimi, P. Honeine, M. Kharouf, C. Richard, J. -Y. Tourneret
Date: 2015-01-22 00:00:00

Linear mixture models are commonly used to represent hyperspectral datacube
as a linear combinations of endmember spectra. However, determining of the
number of endmembers for images embedded in noise is a crucial task. This paper
proposes a fully automatic approach for estimating the number of endmembers in
hyperspectral images. The estimation is based on recent results of random
matrix theory related to the so-called spiked population model. More precisely,
we study the gap between successive eigenvalues of the sample covariance matrix
constructed from high dimensional noisy samples. The resulting estimation
strategy is unsupervised and robust to correlated noise. This strategy is
validated on both synthetic and real images. The experimental results are very
promising and show the accuracy of this algorithm with respect to
state-of-the-art algorithms.
Belief Hidden Markov Model for speech recognition
Authors: Siwar Jendoubi, Boutheina Ben Yaghlane, Arnaud Martin
Date: 2015-01-22 00:00:00
Tasks: model, speech-recognition, Speech Recognition

Speech Recognition searches to predict the spoken words automatically. These
systems are known to be very expensive because of using several pre-recorded
hours of speech. Hence, building a model that minimizes the cost of the
recognizer will be very interesting. In this paper, we present a new approach
for recognizing speech based on belief HMMs instead of proba-bilistic HMMs.
Experiments shows that our belief recognizer is insensitive to the lack of the
data and it can be trained using only one exemplary of each acoustic unit and
it gives a good recognition rates. Consequently, using the belief HMM
recognizer can greatly minimize the cost of these systems.
Bi-l0-l2-Norm Regularization for Blind Motion Deblurring
Authors: Wen-Ze Shao, Hai-Bo Li, Michael Elad
Date: 2014-08-20 00:00:00
Tasks: Computational Efficiency, Deblurring

In blind motion deblurring, leading methods today tend towards highly
non-convex approximations of the l0-norm, especially in the image
regularization term. In this paper, we propose a simple, effective and fast
approach for the estimation of the motion blur-kernel, through a bi-l0-l2-norm
regularization imposed on both the intermediate sharp image and the
blur-kernel. Compared with existing methods, the proposed regularization is
shown to be more effective and robust, leading to a more accurate motion
blur-kernel and a better final restored image. A fast numerical scheme is
deployed for alternatingly computing the sharp image and the blur-kernel, by
coupling the operator splitting and augmented Lagrangian methods. Experimental
results on both a benchmark image dataset and real-world motion blurred images
show that the proposed approach is highly competitive with state-of-the- art
methods in both deblurring effectiveness and computational efficiency.
An Improved Feature Descriptor for Recognition of Handwritten Bangla Alphabet
Authors: Nibaran Das, Subhadip Basu, Ram Sarkar, Mahantapas Kundu, Mita Nasipuri, Dipak Kumar Basu
Date: 2015-01-22 00:00:00

Appropriate feature set for representation of pattern classes is one of the
most important aspects of handwritten character recognition. The effectiveness
of features depends on the discriminating power of the features chosen to
represent patterns of different classes. However, discriminatory features are
not easily measurable. Investigative experimentation is necessary for
identifying discriminatory features. In the present work we have identified a
new variation of feature set which significantly outperforms on handwritten
Bangla alphabet from the previously used feature set. 132 number of features in
all viz. modified shadow features, octant and centroid features, distance based
features, quad tree based longest run features are used here. Using this
feature set the recognition performance increases sharply from the 75.05%
observed in our previous work [7], to 85.40% on 50 character classes with MLP
based classifier on the same dataset.
A GA Based approach for selection of local features for recognition of handwritten Bangla numerals
Authors: Nibaran Das, Subhadip Basu, Punam Kumar Saha, Ram Sarkar, Mahantapas Kundu, Mita Nasipuri
Date: 2015-01-22 00:00:00
Tasks: General Classification, Handwritten Digit Recognition

Soft computing approaches are mainly designed to address the real world
ill-defined, imprecisely formulated problems, combining different kind of novel
models of computation, such as neural networks, genetic algorithms (GAs.
Handwritten digit recognition is a typical example of one such problem. In the
current work we have developed a two-pass approach where the first pass
classifier performs a coarse classification, based on some global features of
the input pattern by restricting the possibility of classification decisions
within a group of classes, smaller than the number of classes considered
initially. In the second pass, the group specific classifiers concentrate on
the features extracted from the selected local regions, and refine the earlier
decision by combining the local and the global features for selecting the true
class of the input pattern from the group of candidate classes selected in the
first pass. To optimize the selection of local regions a GA based approach has
been developed here. The maximum recognition performance on Bangla digit
samples as achieved on the test set, during the first pass of the two pass
approach is 93.35%. After combining the results of the two stage classifiers,
an overall success rate of 95.25% is achieved.
Design of a novel convex hull based feature set for recognition of isolated handwritten Roman numerals
Authors: Nibaran Das, Sandip Pramanik, Subhadip Basu, Punam Kumar Saha, Ram Sarkar, Mahantapas Kundu
Date: 2015-01-22 00:00:00

In this paper, convex hull based features are used for recognition of
isolated Roman numerals using a Multi Layer Perceptron (MLP) based classifier.
Experiments of convex hull based features for handwritten character recognition
are few in numbers. Convex hull of a pattern and the centroid of the convex
hull both are affine invariant attributes. In this work, 25 features are
extracted based on different bays attributes of the convex hull of the digit
patterns. Then these patterns are divided into four sub-images with respect to
the centroid of the convex hull boundary. From each such sub-image 25 bays
features are also calculated. In all 125 convex hull based features are
extracted for each numeric digit patterns under the current experiment. The
performance of the designed feature set is tested on the standard MNIST data
set, consisting of 60000 training and 10000 test images of handwritten Roman
using an MLP based classifier a maximum success rate of 97.44% is achieved on
the test data.
An Algebra to Merge Heterogeneous Classifiers
Authors: Philippe J. Giabbanelli, Joseph G. Peters
Date: 2015-01-21 00:00:00

In distributed classification, each learner observes its environment and
deduces a classifier. As a learner has only a local view of its environment,
classifiers can be exchanged among the learners and integrated, or merged, to
improve accuracy. However, the operation of merging is not defined for most
classifiers. Furthermore, the classifiers that have to be merged may be of
different types in settings such as ad-hoc networks in which several
generations of sensors may be creating classifiers. We introduce decision
spaces as a framework for merging possibly different classifiers. We formally
study the merging operation as an algebra, and prove that it satisfies a
desirable set of properties. The impact of time is discussed for the two main
data mining settings. Firstly, decision spaces can naturally be used with
non-stationary distributions, such as the data collected by sensor networks, as
the impact of a model decays over time. Secondly, we introduce an approach for
stationary distributions, such as homogeneous databases partitioned over
different learners, which ensures that all models have the same impact. We also
present a method that uses storage flexibly to achieve different types of decay
for non-stationary distributions. Finally, we show that the algebraic approach
developed for merging can also be used to analyze the behaviour of other
operators.
Handwritten Devanagari Script Segmentation: A non-linear Fuzzy Approach
Authors: Ram Sarkar, Bibhash Sen, Nibaran Das, Subhadip Basu
Date: 2015-01-22 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

The paper concentrates on improvement of segmentation accuracy by addressing
some of the key challenges of handwritten Devanagari word image segmentation
technique. In the present work, we have developed a new feature based approach
for identification of Matra pixels from a word image, design of a non-linear
fuzzy membership functions for headline estimation and finally design of a
non-linear fuzzy functions for identifying segmentation points on the Matra.
The segmentation accuracy achieved by the current technique is 94.8%. This
shows an improvement of performance by 1.8% over the previous technique [1] on
a 300-word dataset, used for the current experiment.
Efficient Gradient-Based Inference through Transformations between Bayes Nets and Neural Nets
Authors: Diederik P. Kingma, Max Welling
Date: 2014-02-03 00:00:00

Hierarchical Bayesian networks and neural networks with stochastic hidden
units are commonly perceived as two separate types of models. We show that
either of these types of models can often be transformed into an instance of
the other, by switching between centered and differentiable non-centered
parameterizations of the latent variables. The choice of parameterization
greatly influences the efficiency of gradient-based posterior inference; we
show that they are often complementary to eachother, we clarify when each
parameterization is preferred and show how inference can be made robust. In the
non-centered form, a simple Monte Carlo estimator of the marginal likelihood
can be used for learning the parameters. Theoretical results are supported by
experiments.
Point Context: An Effective Shape Descriptor for RST-invariant Trajectory Recognition
Authors: Xingyu Wu, Xia Mao, Lijiang Chen, Yuli Xue, Angelo Compare
Date: 2015-01-22 00:00:00
Tasks: Translation

Motion trajectory recognition is important for characterizing the moving
property of an object. The speed and accuracy of trajectory recognition rely on
a compact and discriminative feature representation, and the situations of
varying rotation, scaling and translation has to be specially considered. In
this paper we propose a novel feature extraction method for trajectories.
Firstly a trajectory is represented by a proposed point context, which is a
rotation-scale-translation (RST) invariant shape descriptor with a flexible
tradeoff between computational complexity and discrimination, yet we prove that
it is a complete shape descriptor. Secondly, the shape context is nonlinearly
mapped to a subspace by kernel nonparametric discriminant analysis (KNDA) to
get a compact feature representation, and thus a trajectory is projected to a
single point in a low-dimensional feature space. Experimental results show
that, the proposed trajectory feature shows encouraging improvement than
state-of-art methods.
Classification of Message Spreading in a Heterogeneous Social Network
Authors: Siwar Jendoubi, Arnaud Martin, Ludovic Liétard, Boutheina Ben Yaghlane
Date: 2015-01-22 00:00:00
Tasks: Classification, General Classification

Nowadays, social networks such as Twitter, Facebook and LinkedIn become
increasingly popular. In fact, they introduced new habits, new ways of
communication and they collect every day several information that have
different sources. Most existing research works fo-cus on the analysis of
homogeneous social networks, i.e. we have a single type of node and link in the
network. However, in the real world, social networks offer several types of
nodes and links. Hence, with a view to preserve as much information as
possible, it is important to consider so-cial networks as heterogeneous and
uncertain. The goal of our paper is to classify the social message based on its
spreading in the network and the theory of belief functions. The proposed
classifier interprets the spread of messages on the network, crossed paths and
types of links. We tested our classifier on a real word network that we
collected from Twitter, and our experiments show the performance of our belief
classifier.
Noisy Sparse Subspace Clustering
Authors: Yu-Xiang Wang, Huan Xu
Date: 2013-09-05 00:00:00
Tasks: Clustering

This paper considers the problem of subspace clustering under noise.
Specifically, we study the behavior of Sparse Subspace Clustering (SSC) when
either adversarial or random noise is added to the unlabelled input data
points, which are assumed to be in a union of low-dimensional subspaces. We
show that a modified version of SSC is \emph{provably effective} in correctly
identifying the underlying subspaces, even with noisy data. This extends
theoretical guarantee of this algorithm to more practical settings and provides
justification to the success of SSC in a class of real applications.
Deep Multimodal Learning for Audio-Visual Speech Recognition
Authors: Youssef Mroueh, Etienne Marcheret, Vaibhava Goel
Date: 2015-01-22 00:00:00
Tasks: Audio-Visual Speech Recognition, Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition, Visual Speech Recognition

In this paper, we present methods in deep multimodal learning for fusing
speech and visual modalities for Audio-Visual Automatic Speech Recognition
(AV-ASR). First, we study an approach where uni-modal deep networks are trained
separately and their final hidden layers fused to obtain a joint feature space
in which another deep network is built. While the audio network alone achieves
a phone error rate (PER) of $41\%$ under clean condition on the IBM large
vocabulary audio-visual studio dataset, this fusion model achieves a PER of
$35.83\%$ demonstrating the tremendous value of the visual channel in phone
classification even in audio with high signal to noise ratio. Second, we
present a new deep network architecture that uses a bilinear softmax layer to
account for class specific correlations between modalities. We show that
combining the posteriors from the bilinear networks with those from the fused
model mentioned above results in a further significant phone error rate
reduction, yielding a final PER of $34.03\%$.
The Loss Surfaces of Multilayer Networks
Authors: Anna Choromanska, Mikael Henaff, Michael Mathieu, Gérard Ben Arous, Yann Lecun
Date: 2014-11-30 00:00:00

We study the connection between the highly non-convex loss function of a
simple model of the fully-connected feed-forward neural network and the
Hamiltonian of the spherical spin-glass model under the assumptions of: i)
variable independence, ii) redundancy in network parametrization, and iii)
uniformity. These assumptions enable us to explain the complexity of the fully
decoupled neural network through the prism of the results from random matrix
theory. We show that for large-size decoupled networks the lowest critical
values of the random loss function form a layered structure and they are
located in a well-defined band lower-bounded by the global minimum. The number
of local minima outside that band diminishes exponentially with the size of the
network. We empirically verify that the mathematical model exhibits similar
behavior as the computer simulations, despite the presence of high dependencies
in real networks. We conjecture that both simulated annealing and SGD converge
to the band of low critical points, and that all critical points found there
are local minima of high quality measured by the test error. This emphasizes a
major difference between large- and small-size networks where for the latter
poor quality local minima have non-zero probability of being recovered.
Finally, we prove that recovering the global minimum becomes harder as the
network size increases and that it is in practice irrelevant as global minimum
often leads to overfitting.
Machine Learning Etudes in Astrophysics: Selection Functions for Mock Cluster Catalogs
Authors: Amir Hajian, Marcelo Alvarez, J. Richard Bond
Date: 2014-09-04 00:00:00
Tasks: BIG-bench Machine Learning

Making mock simulated catalogs is an important component of astrophysical
data analysis. Selection criteria for observed astronomical objects are often
too complicated to be derived from first principles. However the existence of
an observed group of objects is a well-suited problem for machine learning
classification. In this paper we use one-class classifiers to learn the
properties of an observed catalog of clusters of galaxies from ROSAT and to
pick clusters from mock simulations that resemble the observed ROSAT catalog.
We show how this method can be used to study the cross-correlations of thermal
Sunya'ev-Zeldovich signals with number density maps of X-ray selected cluster
catalogs. The method reduces the bias due to hand-tuning the selection function
and is readily scalable to large catalogs with a high-dimensional space of
astrophysical features.
Using Description Logics for RDF Constraint Checking and Closed-World Recognition
Authors: Peter F. Patel-Schneider
Date: 2014-11-15 00:00:00

RDF and Description Logics work in an open-world setting where absence of
information is not information about absence. Nevertheless, Description Logic
axioms can be interpreted in a closed-world setting and in this setting they
can be used for both constraint checking and closed-world recognition against
information sources. When the information sources are expressed in well-behaved
RDF or RDFS (i.e., RDF graphs interpreted in the RDF or RDFS semantics) this
constraint checking and closed-world recognition is simple to describe. Further
this constraint checking can be implemented as SPARQL querying and thus
effectively performed.
On the impact of topological properties of smart grids in power losses optimization problems
Authors: Francesca Possemato, Maurizio Paschero, Lorenzo Livi, Antonello Rizzi, Alireza Sadeghian
Date: 2015-01-19 00:00:00
Tasks: global-optimization

Power losses reduction is one of the main targets for any electrical energy
distribution company. In this paper, we face the problem of joint optimization
of both topology and network parameters in a real smart grid. We consider a
portion of the Italian electric distribution network managed by the ACEA
Distribuzione S.p.A. located in Rome. We perform both the power factor
correction (PFC) for tuning the generators and the distributed feeder
reconfiguration (DFR) to set the state of the breakers. This joint optimization
problem is faced considering a suitable objective function and by adopting
genetic algorithms as global optimization strategy. We analyze admissible
network configurations, showing that some of these violate constraints on
current and voltage at branches and nodes. Such violations depend only on pure
topological properties of the configurations. We perform tests by feeding the
simulation environment with real data concerning hourly samples of dissipated
and generated active and reactive power values of the ACEA smart grid. Results
show that removing the configurations violating the electrical constraints from
the solution space leads to interesting improvements in terms of power loss
reduction. To conclude, we provide also an electrical interpretation of the
phenomenon using graph-based pattern analysis techniques.