pwc-archive/papers-with-abstracts huggingface dataset:
Robust Face Recognition via Adaptive Sparse Representation
Authors: Jing Wang, Can-Yi Lu, Meng Wang, Pei-Pei Li, Shuicheng Yan, Xuegang Hu
Date: 2014-04-18 00:00:00
Tasks: Face Recognition, General Classification, Robust Face Recognition, Sparse Representation-based Classification

Sparse Representation (or coding) based Classification (SRC) has gained great success in face recognition in recent years. However, SRC emphasizes the sparsity too much and overlooks the correlation information which has been demonstrated to be critical in real-world face recognition problems. Besides, some work considers the correlation but overlooks the discriminative ability of sparsity. Different from these existing techniques, in this paper, we propose a framework called Adaptive Sparse Representation based Classification (ASRC) in which sparsity and correlation are jointly considered. Specifically, when the samples are of low correlation, ASRC selects the most discriminative samples for representation, like SRC; when the training samples are highly correlated, ASRC selects most of the correlated and discriminative samples for representation, rather than choosing some related samples randomly. In general, the representation model is adaptive to the correlation structure, which benefits from both $\ell_1$-norm and $\ell_2$-norm. Extensive experiments conducted on publicly available data sets verify the effectiveness and robustness of the proposed algorithm by comparing it with state-of-the-art methods.
Challenges in Persian Electronic Text Analysis
Authors: Behrang QasemiZadeh, Saeed Rahimi, Mehdi Safaee Ghalati
Date: 2014-04-18 00:00:00

Farsi, also known as Persian, is the official language of Iran and Tajikistan
and one of the two main languages spoken in Afghanistan. Farsi enjoys a unified
Arabic script as its writing system. In this paper we briefly introduce the
writing standards of Farsi and highlight problems one would face when analyzing
Farsi electronic texts, especially during development of Farsi corpora
regarding to transcription and encoding of Farsi e-texts. The pointes mentioned
may sounds easy but they are crucial when developing and processing written
corpora of Farsi.
Radical-Enhanced Chinese Character Embedding
Authors: Yaming Sun, Lei Lin, Duyu Tang, Nan Yang, Zhenzhou Ji, Xiaolong Wang
Date: 2014-04-18 00:00:00
Tasks: Chinese Word Segmentation

We present a method to leverage radical for learning Chinese character
embedding. Radical is a semantic and phonetic component of Chinese character.
It plays an important role as characters with the same radical usually have
similar semantic meaning and grammatical usage. However, existing Chinese
processing algorithms typically regard word or character as the basic unit but
ignore the crucial radical information. In this paper, we fill this gap by
leveraging radical for learning continuous representation of Chinese character.
We develop a dedicated neural architecture to effectively learn character
embedding and apply it on Chinese character similarity judgement and Chinese
word segmentation. Experiment results show that our radical-enhanced method
outperforms existing embedding learning algorithms on both tasks.
Two Dimensional Array Imaging with Beam Steered Data
Authors: Sujeet Patole, Murat Torlak
Date: 2013-10-24 00:00:00
Tasks: Quantization, Vocal Bursts Valence Prediction

This paper discusses different approaches used for millimeter wave imaging of
two-dimensional objects. Imaging of a two dimensional object requires reflected
wave data to be collected across two distinct dimensions. In this paper, we
propose a reconstruction method that uses narrowband waveforms along with two
dimensional beam steering. The beam is steered in azimuthal and elevation
direction, which forms the two distinct dimensions required for the
reconstruction. The Reconstruction technique uses inverse Fourier transform
along with amplitude and phase correction factors. In addition, this
reconstruction technique does not require interpolation of the data in either
wavenumber or spatial domain. Use of the two dimensional beam steering offers
better performance in the presence of noise compared with the existing methods,
such as switched array imaging system. Effects of RF impairments such as
quantization of the phase of beam steering weights and timing jitter which add
to phase noise, are analyzed.
Subspace Learning and Imputation for Streaming Big Data Matrices and Tensors
Authors: Morteza Mardani, Gonzalo Mateos, Georgios B. Giannakis
Date: 2014-04-17 00:00:00
Tasks: Imputation

Extracting latent low-dimensional structure from high-dimensional data is of
paramount importance in timely inference tasks encountered with `Big Data'
analytics. However, increasingly noisy, heterogeneous, and incomplete datasets
as well as the need for {\em real-time} processing of streaming data pose major
challenges to this end. In this context, the present paper permeates benefits
from rank minimization to scalable imputation of missing data, via tracking
low-dimensional subspaces and unraveling latent (possibly multi-way) structure
from \emph{incomplete streaming} data. For low-rank matrix data, a subspace
estimator is proposed based on an exponentially-weighted least-squares
criterion regularized with the nuclear norm. After recasting the non-separable
nuclear norm into a form amenable to online optimization, real-time algorithms
with complementary strengths are developed and their convergence is established
under simplifying technical assumptions. In a stationary setting, the
asymptotic estimates obtained offer the well-documented performance guarantees
of the {\em batch} nuclear-norm regularized estimator. Under the same unifying
framework, a novel online (adaptive) algorithm is developed to obtain multi-way
decompositions of \emph{low-rank tensors} with missing entries, and perform
imputation as a byproduct. Simulated tests with both synthetic as well as real
Internet and cardiac magnetic resonance imagery (MRI) data confirm the efficacy
of the proposed algorithms, and their superior performance relative to
state-of-the-art alternatives.
Learning Fine-grained Image Similarity with Deep Ranking
Authors: Jiang Wang, Yang song, Thomas Leung, Chuck Rosenberg, Jinbin Wang, James Philbin, Bo Chen, Ying Wu
Date: 2014-04-17 00:00:00
Proceeding: CVPR 2014 6
Tasks: General Classification, Triplet

Learning fine-grained image similarity is a challenging task. It needs to
capture between-class and within-class image differences. This paper proposes a
deep ranking model that employs deep learning techniques to learn similarity
metric directly from images.It has higher learning capability than models based
on hand-crafted features. A novel multiscale network structure has been
developed to describe the images effectively. An efficient triplet sampling
algorithm is proposed to learn the model with distributed asynchronized
stochastic gradient. Extensive experiments show that the proposed algorithm
outperforms models based on hand-crafted visual features and deep
classification models.
Hierarchical Quasi-Clustering Methods for Asymmetric Networks
Authors: Gunnar Carlsson, Facundo Mémoli, Alejandro Ribeiro, Santiago Segarra
Date: 2014-04-17 00:00:00
Tasks: Clustering

This paper introduces hierarchical quasi-clustering methods, a generalization
of hierarchical clustering for asymmetric networks where the output structure
preserves the asymmetry of the input data. We show that this output structure
is equivalent to a finite quasi-ultrametric space and study admissibility with
respect to two desirable properties. We prove that a modified version of single
linkage is the only admissible quasi-clustering method. Moreover, we show
stability of the proposed method and we establish invariance properties
fulfilled by it. Algorithms are further developed and the value of
quasi-clustering analysis is illustrated with a study of internal migration
within United States.
A New Space for Comparing Graphs
Authors: Anshumali Shrivastava, Ping Li
Date: 2014-04-17 00:00:00
Tasks: Clustering, General Classification

Finding a new mathematical representations for graph, which allows direct
comparison between different graph structures, is an open-ended research
direction. Having such a representation is the first prerequisite for a variety
of machine learning algorithms like classification, clustering, etc., over
graph datasets. In this paper, we propose a symmetric positive semidefinite
matrix with the $(i,j)$-{th} entry equal to the covariance between normalized
vectors $A^ie$ and $A^je$ ($e$ being vector of all ones) as a representation
for graph with adjacency matrix $A$. We show that the proposed matrix
representation encodes the spectrum of the underlying adjacency matrix and it
also contains information about the counts of small sub-structures present in
the graph such as triangles and small paths. In addition, we show that this
matrix is a \emph{"graph invariant"}. All these properties make the proposed
matrix a suitable object for representing graphs.
  The representation, being a covariance matrix in a fixed dimensional metric
space, gives a mathematical embedding for graphs. This naturally leads to a
measure of similarity on graph objects. We define similarity between two given
graphs as a Bhattacharya similarity measure between their corresponding
covariance matrix representations. As shown in our experimental study on the
task of social network classification, such a similarity measure outperforms
other widely used state-of-the-art methodologies. Our proposed method is also
computationally efficient. The computation of both the matrix representation
and the similarity value can be performed in operations linear in the number of
edges. This makes our method scalable in practice.
  We believe our theoretical and empirical results provide evidence for
studying truncated power iterations, of the adjacency matrix, to characterize
social networks.
Multilingual Models for Compositional Distributed Semantics
Authors: Karl Moritz Hermann, Phil Blunsom
Date: 2014-04-17 00:00:00
Proceeding: ACL 2014 6
Tasks: Cross-Lingual Document Classification, Document Classification, General Classification, Learning Semantic Representations

We present a novel technique for learning semantic representations, which
extends the distributional hypothesis to multilingual data and joint-space
embeddings. Our models leverage parallel data and learn to strongly align the
embeddings of semantically equivalent sentences, while maintaining sufficient
distance between those of dissimilar sentences. The models do not rely on word
alignments or any syntactic information and are successfully applied to a
number of diverse languages. We extend our approach to learn semantic
representations at the document level, too. We evaluate these models on two
cross-lingual document classification tasks, outperforming the prior state of
the art. Through qualitative analysis and the study of pivoting effects we
demonstrate that our representations are semantically plausible and can capture
semantic relationships across languages without parallel data.
The Least Wrong Model Is Not in the Data
Authors: Oscar Stiffelman
Date: 2014-04-03 00:00:00

The true process that generated data cannot be determined when multiple
explanations are possible. Prediction requires a model of the probability that
a process, chosen randomly from the set of candidate explanations, generates
some future observation. The best model includes all of the information
contained in the minimal description of the data that is not contained in the
data. It is closely related to the Halting Problem and is logarithmic in the
size of the data. Prediction is difficult because the ideal model is not
computable, and the best computable model is not "findable." However, the error
from any approximation can be bounded by the size of the description using the
model.
Random Projections for Linear Support Vector Machines
Authors: Saurabh Paul, Christos Boutsidis, Malik Magdon-Ismail, Petros Drineas
Date: 2012-11-26 00:00:00
Tasks: Dimensionality Reduction, General Classification, regression

Let X be a data matrix of rank \rho, whose rows represent n points in
d-dimensional space. The linear support vector machine constructs a hyperplane
separator that maximizes the 1-norm soft margin. We develop a new oblivious
dimension reduction technique which is precomputed and can be applied to any
input matrix X. We prove that, with high probability, the margin and minimum
enclosing ball in the feature space are preserved to within \epsilon-relative
error, ensuring comparable generalization as in the original space in the case
of classification. For regression, we show that the margin is preserved to
\epsilon-relative error with high probability. We present extensive experiments
with real and synthetic data to support our theory.
The First Parallel Multilingual Corpus of Persian: Toward a Persian BLARK
Authors: Behrang Qasemizadeh, Saeed Rahimi, Behrooz Mahmoodi Bakhtiari
Date: 2014-04-17 00:00:00

In this article, we have introduced the first parallel corpus of Persian with
more than 10 other European languages. This article describes primary steps
toward preparing a Basic Language Resources Kit (BLARK) for Persian. Up to now,
we have proposed morphosyntactic specification of Persian based on
EAGLE/MULTEXT guidelines and specific resources of MULTEXT-East. The article
introduces Persian Language, with emphasis on its orthography and
morphosyntactic features, then a new Part-of-Speech categorization and
orthography for Persian in digital environments is proposed. Finally, the
corpus and related statistic will be analyzed.
Discovering and Exploiting Entailment Relationships in Multi-Label Learning
Authors: Christina Papagiannopoulou, Grigorios Tsoumakas, Ioannis Tsamardinos
Date: 2014-04-15 00:00:00
Tasks: Multi-Label Learning

This work presents a sound probabilistic method for enforcing adherence of
the marginal probabilities of a multi-label model to automatically discovered
deterministic relationships among labels. In particular we focus on discovering
two kinds of relationships among the labels. The first one concerns pairwise
positive entailement: pairs of labels, where the presence of one implies the
presence of the other in all instances of a dataset. The second concerns
exclusion: sets of labels that do not coexist in the same instances of the
dataset. These relationships are represented with a Bayesian network. Marginal
probabilities are entered as soft evidence in the network and adjusted through
probabilistic inference. Our approach offers robust improvements in mean
average precision compared to the standard binary relavance approach across all
12 datasets involved in our experiments. The discovery process helps
interesting implicit knowledge to emerge, which could be useful in itself.
Generalized Evidence Theory
Authors: Yong Deng
Date: 2014-04-17 00:00:00
Tasks: Management

Conflict management is still an open issue in the application of Dempster
Shafer evidence theory. A lot of works have been presented to address this
issue. In this paper, a new theory, called as generalized evidence theory
(GET), is proposed. Compared with existing methods, GET assumes that the
general situation is in open world due to the uncertainty and incomplete
knowledge. The conflicting evidence is handled under the framework of GET. It
is shown that the new theory can explain and deal with the conflicting evidence
in a more reasonable way.
Rate-Distortion Auto-Encoders
Authors: Luis G. Sanchez Giraldo, Jose C. Principe
Date: 2013-12-28 00:00:00

A rekindled the interest in auto-encoder algorithms has been spurred by
recent work on deep learning. Current efforts have been directed towards
effective training of auto-encoder architectures with a large number of coding
units. Here, we propose a learning algorithm for auto-encoders based on a
rate-distortion objective that minimizes the mutual information between the
inputs and the outputs of the auto-encoder subject to a fidelity constraint.
The goal is to learn a representation that is minimally committed to the input
data, but that is rich enough to reconstruct the inputs up to certain level of
distortion. Minimizing the mutual information acts as a regularization term
whereas the fidelity constraint can be understood as a risk functional in the
conventional statistical learning setting. The proposed algorithm uses a
recently introduced measure of entropy based on infinitely divisible matrices
that avoids the plug in estimation of densities. Experiments using
over-complete bases show that the rate-distortion auto-encoders can learn a
regularized input-output mapping in an implicit manner.
A Survey of Data Mining Techniques for Social Media Analysis
Authors: Mariam Adedoyin-Olowe, Mohamed Medhat Gaber, Frederic Stahl
Date: 2013-12-17 00:00:00
Tasks: Information Retrieval, Retrieval, Survey

Social network has gained remarkable attention in the last decade. Accessing
social network sites such as Twitter, Facebook LinkedIn and Google+ through the
internet and the web 2.0 technologies has become more affordable. People are
becoming more interested in and relying on social network for information, news
and opinion of other users on diverse subject matters. The heavy reliance on
social network sites causes them to generate massive data characterised by
three computational issues namely; size, noise and dynamism. These issues often
make social network data very complex to analyse manually, resulting in the
pertinent use of computational means of analysing them. Data mining provides a
wide range of techniques for detecting useful knowledge from massive datasets
like trends, patterns and rules [44]. Data mining techniques are used for
information retrieval, statistical modelling and machine learning. These
techniques employ data pre-processing, data analysis, and data interpretation
processes in the course of data analysis. This survey discusses different data
mining techniques used in mining diverse aspects of the social network over
decades going from the historical techniques to the up-to-date models,
including our novel technique named TRCM. All the techniques covered in this
survey are listed in the Table.1 including the tools employed as well as names
of their authors.
Partially Observed, Multi-objective Markov Games
Authors: Yanling Chang, Alan L. Erera, Chelsea C. White III
Date: 2014-04-16 00:00:00

The intent of this research is to generate a set of non-dominated policies
from which one of two agents (the leader) can select a most preferred policy to
control a dynamic system that is also affected by the control decisions of the
other agent (the follower). The problem is described by an infinite horizon,
partially observed Markov game (POMG). At each decision epoch, each agent
knows: its past and present states, its past actions, and noise corrupted
observations of the other agent's past and present states. The actions of each
agent are determined at each decision epoch based on these data. The leader
considers multiple objectives in selecting its policy. The follower considers a
single objective in selecting its policy with complete knowledge of and in
response to the policy selected by the leader. This leader-follower assumption
allows the POMG to be transformed into a specially structured, partially
observed Markov decision process (POMDP). This POMDP is used to determine the
follower's best response policy. A multi-objective genetic algorithm (MOGA) is
used to create the next generation of leader policies based on the fitness
measures of each leader policy in the current generation. Computing a fitness
measure for a leader policy requires a value determination calculation, given
the leader policy and the follower's best response policy. The policies from
which the leader can select a most preferred policy are the non-dominated
policies of the final generation of leader policies created by the MOGA. An
example is presented that illustrates how these results can be used to support
a manager of a liquid egg production process (the leader) in selecting a
sequence of actions to best control this process over time, given that there is
an attacker (the follower) who seeks to contaminate the liquid egg production
process with a chemical or biological toxin.
Automatic Annotation of Axoplasmic Reticula in Pursuit of Connectomes using High-Resolution Neural EM Data
Authors: Ayushi Sinha, William Gray Roncal, Narayanan Kasthuri, Jeff W. Lichtman, Randal Burns, Michael Kazhdan
Date: 2014-04-16 00:00:00
Tasks: Segmentation

Accurately estimating the wiring diagram of a brain, known as a connectome,
at an ultrastructure level is an open research problem. Specifically, precisely
tracking neural processes is difficult, especially across many image slices.
Here, we propose a novel method to automatically identify and annotate small
subcellular structures present in axons, known as axoplasmic reticula, through
a 3D volume of high-resolution neural electron microscopy data. Our method
produces high precision annotations, which can help improve automatic
segmentation by using our results as seeds for segmentation, and as cues to aid
segment merging.
Automatic Annotation of Axoplasmic Reticula in Pursuit of Connectomes
Authors: Ayushi Sinha, William Gray Roncal, Narayanan Kasthuri, Ming Chuang, Priya Manavalan, Dean M. Kleissas, Joshua T. Vogelstein, R. Jacob Vogelstein, Randal Burns, Jeff W. Lichtman, Michael Kazhdan
Date: 2014-04-16 00:00:00

In this paper, we present a new pipeline which automatically identifies and
annotates axoplasmic reticula, which are small subcellular structures present
only in axons. We run our algorithm on the Kasthuri11 dataset, which was color
corrected using gradient-domain techniques to adjust contrast. We use a
bilateral filter to smooth out the noise in this data while preserving edges,
which highlights axoplasmic reticula. These axoplasmic reticula are then
annotated using a morphological region growing algorithm. Additionally, we
perform Laplacian sharpening on the bilaterally filtered data to enhance edges,
and repeat the morphological region growing algorithm to annotate more
axoplasmic reticula. We track our annotations through the slices to improve
precision, and to create long objects to aid in segment merging. This method
annotates axoplasmic reticula with high precision. Our algorithm can easily be
adapted to annotate axoplasmic reticula in different sets of brain data by
changing a few thresholds. The contribution of this work is the introduction of
a straightforward and robust pipeline which annotates axoplasmic reticula with
high precision, contributing towards advancements in automatic feature
annotations in neural EM data.
Stable Graphical Models
Authors: Navodit Misra, Ercan E. Kuruoglu
Date: 2014-04-16 00:00:00

Stable random variables are motivated by the central limit theorem for
densities with (potentially) unbounded variance and can be thought of as
natural generalizations of the Gaussian distribution to skewed and heavy-tailed
phenomenon. In this paper, we introduce stable graphical (SG) models, a class
of multivariate stable densities that can also be represented as Bayesian
networks whose edges encode linear dependencies between random variables. One
major hurdle to the extensive use of stable distributions is the lack of a
closed-form analytical expression for their densities. This makes penalized
maximum-likelihood based learning computationally demanding. We establish
theoretically that the Bayesian information criterion (BIC) can asymptotically
be reduced to the computationally more tractable minimum dispersion criterion
(MDC) and develop StabLe, a structure learning algorithm based on MDC. We use
simulated datasets for five benchmark network topologies to empirically
demonstrate how StabLe improves upon ordinary least squares (OLS) regression.
We also apply StabLe to microarray gene expression data for lymphoblastoid
cells from 727 individuals belonging to eight global population groups. We
establish that StabLe improves test set performance relative to OLS via
ten-fold cross-validation. Finally, we develop SGEX, a method for quantifying
differential expression of genes between different population groups.