pwc-archive/papers-with-abstracts huggingface dataset:
Adaptive Learning of Region-based pLSA Model for Total Scene Annotation
Authors: Yuzhu Zhou, Le Li, Honggang Zhang
Date: 2013-11-21 00:00:00
Tasks: TAG

In this paper, we present a region-based pLSA model to accomplish the task of
total scene annotation. To be more specific, we not only properly generate a
list of tags for each image, but also localizing each region with its
corresponding tag. We integrate advantages of different existing region-based
works: employ efficient and powerful JSEG algorithm for segmentation so that
each region can easily express meaningful object information; the introduction
of pLSA model can help better capturing semantic information behind the
low-level features. Moreover, we also propose an adaptive padding mechanism to
automatically choose the optimal padding strategy for each region, which
directly increases the overall system performance. Finally we conduct 3
experiments to verify our ideas on Corel database and demonstrate the
effectiveness and accuracy of our system.
Covariance Estimation in High Dimensions via Kronecker Product Expansions
Authors: Theodoros Tsiligkaridis, Alfred O. Hero III
Date: 2013-02-12 00:00:00
Tasks: Vocal Bursts Intensity Prediction

This paper presents a new method for estimating high dimensional covariance
matrices. The method, permuted rank-penalized least-squares (PRLS), is based on
a Kronecker product series expansion of the true covariance matrix. Assuming an
i.i.d. Gaussian random sample, we establish high dimensional rates of
convergence to the true covariance as both the number of samples and the number
of variables go to infinity. For covariance matrices of low separation rank,
our results establish that PRLS has significantly faster convergence than the
standard sample covariance matrix (SCM) estimator. The convergence rate
captures a fundamental tradeoff between estimation error and approximation
error, thus providing a scalable covariance estimation framework in terms of
separation rank, similar to low rank approximation of covariance matrices. The
MSE convergence rates generalize the high dimensional rates recently obtained
for the ML Flip-flop algorithm for Kronecker product covariance estimation. We
show that a class of block Toeplitz covariance matrices is approximatable by
low separation rank and give bounds on the minimal separation rank $r$ that
ensures a given level of bias. Simulations are presented to validate the
theoretical bounds. As a real world application, we illustrate the utility of
the proposed Kronecker covariance estimator for spatio-temporal linear least
squares prediction of multivariate wind speed measurements.
A Unified SVM Framework for Signal Estimation
Authors: José Luis Rojo-Álvarez, Manel Martínez-Ramón, Jordi Muñoz-Marí, Gustavo Camps-Valls
Date: 2013-11-21 00:00:00
Tasks: Time Series Analysis

This paper presents a unified framework to tackle estimation problems in
Digital Signal Processing (DSP) using Support Vector Machines (SVMs). The use
of SVMs in estimation problems has been traditionally limited to its mere use
as a black-box model. Noting such limitations in the literature, we take
advantage of several properties of Mercer's kernels and functional analysis to
develop a family of SVM methods for estimation in DSP. Three types of signal
model equations are analyzed. First, when a specific time-signal structure is
assumed to model the underlying system that generated the data, the linear
signal model (so called Primal Signal Model formulation) is first stated and
analyzed. Then, non-linear versions of the signal structure can be readily
developed by following two different approaches. On the one hand, the signal
model equation is written in reproducing kernel Hilbert spaces (RKHS) using the
well-known RKHS Signal Model formulation, and Mercer's kernels are readily used
in SVM non-linear algorithms. On the other hand, in the alternative and not so
common Dual Signal Model formulation, a signal expansion is made by using an
auxiliary signal model equation given by a non-linear regression of each time
instant in the observed time series. These building blocks can be used to
generate different novel SVM-based methods for problems of signal estimation,
and we deal with several of the most important ones in DSP. We illustrate the
usefulness of this methodology by defining SVM algorithms for linear and
non-linear system identification, spectral analysis, nonuniform interpolation,
sparse deconvolution, and array processing. The performance of the developed
SVM methods is compared to standard approaches in all these settings. The
experimental results illustrate the generality, simplicity, and capabilities of
the proposed SVM framework for DSP.
Nonparametric Bayesian models of hierarchical structure in complex networks
Authors: Mikkel N. Schmidt, Tue Herlau, Morten Mørup
Date: 2013-11-05 00:00:00

Analyzing and understanding the structure of complex relational data is
important in many applications including analysis of the connectivity in the
human brain. Such networks can have prominent patterns on different scales,
calling for a hierarchically structured model. We propose two non-parametric
Bayesian hierarchical network models based on Gibbs fragmentation tree priors,
and demonstrate their ability to capture nested patterns in simulated networks.
On real networks we demonstrate detection of hierarchical structure and show
predictive performance on par with the state of the art. We envision that our
methods can be employed in exploratory analysis of large scale complex networks
for example to model human brain connectivity.
Dealing with the Fuzziness of Human Reasoning
Authors: Michael Gr. Voskoglou, Igor Ya. Subbotin
Date: 2013-11-21 00:00:00

Reasoning, the most important human brain operation, is charactrized by a
degree fuzziness. In the present paper we construct a fuzzy model for the
reasoning process giving through the calculation of the possibilities of all
possible individuals' profiles a quantitative/qualitative view of their
behaviour during the above process and we use the centroid defuzzification
technique for measuring the reasoning skills. We also present a number of
classroom experiments illustrating our results in practice.
Comparative Study Of Image Edge Detection Algorithms
Authors: Shubham Saini, Bhavesh Kasliwal, Shraey Bhatia
Date: 2013-11-20 00:00:00
Tasks: Edge Detection, General Classification, Object, object-detection, Object Detection

Since edge detection is in the forefront of image processing for object
detection, it is crucial to have a good understanding of edge detection
algorithms. The reason for this is that edges form the outline of an object. An
edge is the boundary between an object and the background, and indicates the
boundary between overlapping objects. This means that if the edges in an image
can be identified accurately, all of the objects can be located and basic
properties such as area, perimeter, and shape can be measured. Since computer
vision involves the identification and classification of objects in an image,
edge detection is an essential tool. We tested two edge detectors that use
different methods for detecting edges and compared their results under a
variety of situations to determine which detector was preferable under
different sets of conditions.
Texture descriptor combining fractal dimension and artificial crawlers
Authors: Wesley Nunes Gonçalves, Bruno Brandoli Machado, Odemir Martinez Bruno
Date: 2013-11-21 00:00:00
Tasks: Attribute, Texture Classification

Texture is an important visual attribute used to describe images. There are
many methods available for texture analysis. However, they do not capture the
details richness of the image surface. In this paper, we propose a new method
to describe textures using the artificial crawler model. This model assumes
that each agent can interact with the environment and each other. Since this
swarm system alone does not achieve a good discrimination, we developed a new
method to increase the discriminatory power of artificial crawlers, together
with the fractal dimension theory. Here, we estimated the fractal dimension by
the Bouligand-Minkowski method due to its precision in quantifying structural
properties of images. We validate our method on two texture datasets and the
experimental results reveal that our method leads to highly discriminative
textural features. The results indicate that our method can be used in
different texture applications.
Robust Compressed Sensing and Sparse Coding with the Difference Map
Authors: Will Landecker, Rick Chartrand, Simon DeDeo
Date: 2013-10-31 00:00:00
Tasks: compressed sensing, Image Reconstruction

In compressed sensing, we wish to reconstruct a sparse signal $x$ from
observed data $y$. In sparse coding, on the other hand, we wish to find a
representation of an observed signal $y$ as a sparse linear combination, with
coefficients $x$, of elements from an overcomplete dictionary. While many
algorithms are competitive at both problems when $x$ is very sparse, it can be
challenging to recover $x$ when it is less sparse. We present the Difference
Map, which excels at sparse recovery when sparseness is lower and noise is
higher. The Difference Map out-performs the state of the art with
reconstruction from random measurements and natural image reconstruction via
sparse coding.
Streaming Variational Bayes
Authors: Tamara Broderick, Nicholas Boyd, Andre Wibisono, Ashia C. Wilson, Michael. I. Jordan
Date: 2013-07-25 00:00:00
Proceeding: NeurIPS 2013 12
Tasks: Variational Inference

We present SDA-Bayes, a framework for (S)treaming, (D)istributed,
(A)synchronous computation of a Bayesian posterior. The framework makes
streaming updates to the estimated posterior according to a user-specified
approximation batch primitive. We demonstrate the usefulness of our framework,
with variational Bayes (VB) as the primitive, by fitting the latent Dirichlet
allocation model to two large-scale document collections. We demonstrate the
advantages of our algorithm over stochastic variational inference (SVI) by
comparing the two after a single pass through a known amount of data---a case
where SVI may be applied---and in the streaming setting, where SVI does not
apply.
A survey on independence-based Markov networks learning
Authors: Federico Schlüter
Date: 2011-08-10 00:00:00
Tasks: BIG-bench Machine Learning, Survey

This work reports the most relevant technical aspects in the problem of
learning the \emph{Markov network structure} from data. Such problem has become
increasingly important in machine learning, and many other application fields
of machine learning. Markov networks, together with Bayesian networks, are
probabilistic graphical models, a widely used formalism for handling
probability distributions in intelligent systems. Learning graphical models
from data have been extensively applied for the case of Bayesian networks, but
for Markov networks learning it is not tractable in practice. However, this
situation is changing with time, given the exponential growth of computers
capacity, the plethora of available digital data, and the researching on new
learning technologies. This work stresses on a technology called
independence-based learning, which allows the learning of the independence
structure of those networks from data in an efficient and sound manner,
whenever the dataset is sufficiently large, and data is a representative
sampling of the target distribution. In the analysis of such technology, this
work surveys the current state-of-the-art algorithms for learning Markov
networks structure, discussing its current limitations, and proposing a series
of open problems where future works may produce some advances in the area in
terms of quality and efficiency. The paper concludes by opening a discussion
about how to develop a general formalism for improving the quality of the
structures learned, when data is scarce.
Classification with Scattering Operators
Authors: Joan Bruna, Stéphane Mallat
Date: 2010-11-12 00:00:00
Tasks: Classification, General Classification, Handwritten Digit Recognition, Model Selection, Texture Classification, Translation

A scattering vector is a local descriptor including multiscale and
multi-direction co-occurrence information. It is computed with a cascade of
wavelet decompositions and complex modulus. This scattering representation is
locally translation invariant and linearizes deformations. A supervised
classification algorithm is computed with a PCA model selection on scattering
vectors. State of the art results are obtained for handwritten digit
recognition and texture classification.
Gromov-Hausdorff stability of linkage-based hierarchical clustering methods
Authors: A. Martínez-Pérez
Date: 2013-11-20 00:00:00
Tasks: Clustering

A hierarchical clustering method is stable if small perturbations on the data
set produce small perturbations in the result. These perturbations are measured
using the Gromov-Hausdorff metric. We study the problem of stability on
linkage-based hierarchical clustering methods. We obtain that, under some basic
conditions, standard linkage-based methods are semi-stable. This means that
they are stable if the input data is close enough to an ultrametric space. We
prove that, apart from exotic examples, introducing any unchaining condition in
the algorithm always produces unstable methods.
Mapping cognitive ontologies to and from the brain
Authors: Yannick Schwartz, Bertrand Thirion, Gaël Varoquaux
Date: 2013-11-15 00:00:00

Imaging neuroscience links brain activation maps to behavior and cognition
via correlational studies. Due to the nature of the individual experiments,
based on eliciting neural response from a small number of stimuli, this link is
incomplete, and unidirectional from the causal point of view. To come to
conclusions on the function implied by the activation of brain regions, it is
necessary to combine a wide exploration of the various brain functions and some
inversion of the statistical inference. Here we introduce a methodology for
accumulating knowledge towards a bidirectional link between observed brain
activity and the corresponding function. We rely on a large corpus of imaging
studies and a predictive engine. Technically, the challenges are to find
commonality between the studies without denaturing the richness of the corpus.
The key elements that we contribute are labeling the tasks performed with a
cognitive ontology, and modeling the long tail of rare paradigms in the corpus.
To our knowledge, our approach is the first demonstration of predicting the
cognitive content of completely new brain images. To that end, we propose a
method that predicts the experimental paradigms across different studies.
Optimal classification in sparse Gaussian graphic model
Authors: Yingying Fan, Jiashun Jin, Zhigang Yao
Date: 2012-12-21 00:00:00
Tasks: Classification, General Classification, Math, model

Consider a two-class classification problem where the number of features is
much larger than the sample size. The features are masked by Gaussian noise
with mean zero and covariance matrix $\Sigma$, where the precision matrix
$\Omega=\Sigma^{-1}$ is unknown but is presumably sparse. The useful features,
also unknown, are sparse and each contributes weakly (i.e., rare and weak) to
the classification decision. By obtaining a reasonably good estimate of
$\Omega$, we formulate the setting as a linear regression model. We propose a
two-stage classification method where we first select features by the method of
Innovated Thresholding (IT), and then use the retained features and Fisher's
LDA for classification. In this approach, a crucial problem is how to set the
threshold of IT. We approach this problem by adapting the recent innovation of
Higher Criticism Thresholding (HCT). We find that when useful features are rare
and weak, the limiting behavior of HCT is essentially just as good as the
limiting behavior of ideal threshold, the threshold one would choose if the
underlying distribution of the signals is known (if only). Somewhat
surprisingly, when $\Omega$ is sufficiently sparse, its off-diagonal
coordinates usually do not have a major influence over the classification
decision. Compared to recent work in the case where $\Omega$ is the identity
matrix [Proc. Natl. Acad. Sci. USA 105 (2008) 14790-14795; Philos. Trans. R.
Soc. Lond. Ser. A Math. Phys. Eng. Sci. 367 (2009) 4449-4470], the current
setting is much more general, which needs a new approach and much more
sophisticated analysis. One key component of the analysis is the intimate
relationship between HCT and Fisher's separation. Another key component is the
tight large-deviation bounds for empirical processes for data with
unconventional correlation structures, where graph theory on vertex coloring
plays an important role.
Analyzing Evolutionary Optimization in Noisy Environments
Authors: Chao Qian, Yang Yu, Zhi-Hua Zhou
Date: 2013-11-20 00:00:00
Tasks: Evolutionary Algorithms

Many optimization tasks have to be handled in noisy environments, where we
cannot obtain the exact evaluation of a solution but only a noisy one. For
noisy optimization tasks, evolutionary algorithms (EAs), a kind of stochastic
metaheuristic search algorithm, have been widely and successfully applied.
Previous work mainly focuses on empirical studying and designing EAs for noisy
optimization, while, the theoretical counterpart has been little investigated.
In this paper, we investigate a largely ignored question, i.e., whether an
optimization problem will always become harder for EAs in a noisy environment.
We prove that the answer is negative, with respect to the measurement of the
expected running time. The result implies that, for optimization tasks that
have already been quite hard to solve, the noise may not have a negative
effect, and the easier a task the more negatively affected by the noise. On a
representative problem where the noise has a strong negative effect, we examine
two commonly employed mechanisms in EAs dealing with noise, the re-evaluation
and the threshold selection strategies. The analysis discloses that the two
strategies, however, both are not effective, i.e., they do not make the EA more
noise tolerant. We then find that a small modification of the threshold
selection allows it to be proven as an effective strategy for dealing with the
noise in the problem.
Neural Network Application on Foliage Plant Identification
Authors: Abdul Kadir, Lukito Edi Nugroho, Adhi Susanto, Paulus Insap Santosa
Date: 2013-11-20 00:00:00
Tasks: Attribute

Several researches in leaf identification did not include color information
as features. The main reason is caused by a fact that they used green colored
leaves as samples. However, for foliage plants, plants with colorful leaves,
fancy patterns in their leaves, and interesting plants with unique shape, color
and also texture could not be neglected. For example, Epipremnum pinnatum
'Aureum' and Epipremnum pinnatum 'Marble Queen' have similar patterns, same
shape, but different colors. Combination of shape, color, texture features, and
other attribute contained on the leaf is very useful in leaf identification. In
this research, Polar Fourier Transform and three kinds of geometric features
were used to represent shape features, color moments that consist of mean,
standard deviation, skewness were used to represent color features, texture
features are extracted from GLCMs, and vein features were added to improve
performance of the identification system. The identification system uses
Probabilistic Neural Network (PNN) as a classifier. The result shows that the
system gives average accuracy of 93.0833% for 60 kinds of foliage plants.
Leaf Classification Using Shape, Color, and Texture Features
Authors: Abdul Kadir, Lukito Edi Nugroho, Adhi Susanto, Paulus Insap Santosa
Date: 2013-11-20 00:00:00
Tasks: Classification, General Classification

Several methods to identify plants have been proposed by several researchers.
Commonly, the methods did not capture color information, because color was not
recognized as an important aspect to the identification. In this research,
shape and vein, color, and texture features were incorporated to classify a
leaf. In this case, a neural network called Probabilistic Neural network (PNN)
was used as a classifier. The experimental result shows that the method for
classification gives average accuracy of 93.75% when it was tested on Flavia
dataset, that contains 32 kinds of plant leaves. It means that the method gives
better performance compared to the original work.
Experiments of Distance Measurements in a Foliage Plant Retrieval System
Authors: Abdul Kadir, Lukito Edi Nugroho, Adhi Susanto, Paulus Insap Santosa
Date: 2013-11-20 00:00:00
Tasks: Image Retrieval, Retrieval

One of important components in an image retrieval system is selecting a
distance measure to compute rank between two objects. In this paper, several
distance measures were researched to implement a foliage plant retrieval
system. Sixty kinds of foliage plants with various leaf color and shape were
used to test the performance of 7 different kinds of distance measures: city
block distance, Euclidean distance, Canberra distance, Bray-Curtis distance, x2
statistics, Jensen Shannon divergence and Kullback Leibler divergence. The
results show that city block and Euclidean distance measures gave the best
performance among the others.
Hypothesis Testing for Automated Community Detection in Networks
Authors: Peter J. Bickel, Purnamrita Sarkar
Date: 2013-11-12 00:00:00
Tasks: Clustering, Community Detection, Two-sample testing

Community detection in networks is a key exploratory tool with applications
in a diverse set of areas, ranging from finding communities in social and
biological networks to identifying link farms in the World Wide Web. The
problem of finding communities or clusters in a network has received much
attention from statistics, physics and computer science. However, most
clustering algorithms assume knowledge of the number of clusters k. In this
paper we propose to automatically determine k in a graph generated from a
Stochastic Blockmodel. Our main contribution is twofold; first, we
theoretically establish the limiting distribution of the principal eigenvalue
of the suitably centered and scaled adjacency matrix, and use that distribution
for our hypothesis test. Secondly, we use this test to design a recursive
bipartitioning algorithm. Using quantifiable classification tasks on real world
networks with ground truth, we show that our algorithm outperforms existing
probabilistic models for learning overlapping clusters, and on unlabeled
networks, we show that we uncover nested community structure.
Domain Adaptation of Majority Votes via Perturbed Variation-based Label Transfer
Authors: Emilie Morvant
Date: 2013-11-19 00:00:00
Tasks: Domain Adaptation

We tackle the PAC-Bayesian Domain Adaptation (DA) problem. This arrives when
one desires to learn, from a source distribution, a good weighted majority vote
(over a set of classifiers) on a different target distribution. In this
context, the disagreement between classifiers is known crucial to control. In
non-DA supervised setting, a theoretical bound - the C-bound - involves this
disagreement and leads to a majority vote learning algorithm: MinCq. In this
work, we extend MinCq to DA by taking advantage of an elegant divergence
between distribution called the Perturbed Varation (PV). Firstly, justified by
a new formulation of the C-bound, we provide to MinCq a target sample labeled
thanks to a PV-based self-labeling focused on regions where the source and
target marginal distributions are closer. Secondly, we propose an original
process for tuning the hyperparameters. Our framework shows very promising
results on a toy problem.