pwc-archive/papers-with-abstracts huggingface dataset:
On the Use of Different Feature Extraction Methods for Linear and Non Linear kernels
Authors: Imen Trabelsi, Dorra Ben Ayed
Date: 2014-06-27 00:00:00
Tasks: Robust Speech Recognition, Speaker Identification, speech-recognition, Speech Recognition

The speech feature extraction has been a key focus in robust speech
recognition research; it significantly affects the recognition performance. In
this paper, we first study a set of different features extraction methods such
as linear predictive coding (LPC), mel frequency cepstral coefficient (MFCC)
and perceptual linear prediction (PLP) with several features normalization
techniques like rasta filtering and cepstral mean subtraction (CMS). Based on
this, a comparative evaluation of these features is performed on the task of
text independent speaker identification using a combination between gaussian
mixture models (GMM) and linear and non-linear kernels based on support vector
machine (SVM).
Fourier PCA and Robust Tensor Decomposition
Authors: Navin Goyal, Santosh Vempala, Ying Xiao
Date: 2013-06-25 00:00:00
Tasks: Tensor Decomposition

Fourier PCA is Principal Component Analysis of a matrix obtained from higher
order derivatives of the logarithm of the Fourier transform of a
distribution.We make this method algorithmic by developing a tensor
decomposition method for a pair of tensors sharing the same vectors in rank-$1$
decompositions. Our main application is the first provably polynomial-time
algorithm for underdetermined ICA, i.e., learning an $n \times m$ matrix $A$
from observations $y=Ax$ where $x$ is drawn from an unknown product
distribution with arbitrary non-Gaussian components. The number of component
distributions $m$ can be arbitrarily higher than the dimension $n$ and the
columns of $A$ only need to satisfy a natural and efficiently verifiable
nondegeneracy condition. As a second application, we give an alternative
algorithm for learning mixtures of spherical Gaussians with linearly
independent means. These results also hold in the presence of Gaussian noise.
A Multi Level Data Fusion Approach for Speaker Identification on Telephone Speech
Authors: Imen Trabelsi, Dorra Ben Ayed
Date: 2014-06-27 00:00:00
Tasks: Speaker Identification

Several speaker identification systems are giving good performance with clean
speech but are affected by the degradations introduced by noisy audio
conditions. To deal with this problem, we investigate the use of complementary
information at different levels for computing a combined match score for the
unknown speaker. In this work, we observe the effect of two supervised machine
learning approaches including support vectors machines (SVM) and na\"ive bayes
(NB). We define two feature vector sets based on mel frequency cepstral
coefficients (MFCC) and relative spectral perceptual linear predictive
coefficients (RASTA-PLP). Each feature is modeled using the Gaussian Mixture
Model (GMM). Several ways of combining these information sources give
significant improvements in a text-independent speaker identification task
using a very large telephone degraded NTIMIT database.
Neighborhood filters and the decreasing rearrangement
Authors: Gonzalo Galiano, Julián Velasco
Date: 2013-11-09 00:00:00
Tasks: Denoising, Image Denoising

Nonlocal filters are simple and powerful techniques for image denoising. In
this paper, we give new insights into the analysis of one kind of them, the
Neighborhood filter, by using a classical although not very common
transformation: the decreasing rearrangement of a function (the image).
Independently of the dimension of the image, we reformulate the Neighborhood
filter and its iterative variants as an integral operator defined in a
one-dimensional space. The simplicity of this formulation allows to perform a
detailed analysis of its properties. Among others, we prove that the filter
behaves asymptotically as a shock filter combined with a border diffusive term,
responsible for the staircaising effect and the loss of contrast.
A Novel Method for Comparative Analysis of DNA Sequences by Ramanujan-Fourier Transform
Authors: Changchuan Yin, Xuemeng E. Yin, Jiasong Wang
Date: 2014-03-06 00:00:00
Tasks: Clustering, Multiple Sequence Alignment

Alignment-free sequence analysis approaches provide important alternatives
over multiple sequence alignment (MSA) in biological sequence analysis because
alignment-free approaches have low computation complexity and are not dependent
on high level of sequence identity, however, most of the existing
alignment-free methods do not employ true full information content of sequences
and thus can not accurately reveal similarities and differences among DNA
sequences. We present a novel alignment-free computational method for sequence
analysis based on Ramanujan-Fourier transform (RFT), in which complete
information of DNA sequences is retained. We represent DNA sequences as four
binary indicator sequences and apply RFT on the indicator sequences to convert
them into frequency domain. The Euclidean distance of the complete RFT
coefficients of DNA sequences are used as similarity measure. To address the
different lengths in Euclidean space of RFT coefficients, we pad zeros to short
DNA binary sequences so that the binary sequences equal the longest length in
the comparison sequence data. Thus, the DNA sequences are compared in the same
dimensional frequency space without information loss. We demonstrate the
usefulness of the proposed method by presenting experimental results on
hierarchical clustering of genes and genomes. The proposed method opens a new
channel to biological sequence analysis, classification, and structural module
identification.
Inducing Language Networks from Continuous Space Word Representations
Authors: Bryan Perozzi, Rami Al-Rfou, Vivek Kulkarni, Steven Skiena
Date: 2014-03-06 00:00:00

Recent advancements in unsupervised feature learning have developed powerful
latent representations of words. However, it is still not clear what makes one
representation better than another and how we can learn the ideal
representation. Understanding the structure of latent spaces attained is key to
any future advancement in unsupervised learning. In this work, we introduce a
new view of continuous space word representations as language networks. We
explore two techniques to create language networks from learned features by
inducing them for two popular word representation methods and examining the
properties of their resulting networks. We find that the induced networks
differ from other methods of creating language networks, and that they contain
meaningful community structure.
Polyglot: Distributed Word Representations for Multilingual NLP
Authors: Rami Al-Rfou, Bryan Perozzi, Steven Skiena
Date: 2013-07-05 00:00:00
Proceeding: WS 2013 8
Tasks: Language Modeling, Language Modelling, Multilingual NLP, Word Embeddings

Distributed word representations (word embeddings) have recently contributed
to competitive performance in language modeling and several NLP tasks. In this
work, we train word embeddings for more than 100 languages using their
corresponding Wikipedias. We quantitatively demonstrate the utility of our word
embeddings by using them as the sole features for training a part of speech
tagger for a subset of these languages. We find their performance to be
competitive with near state-of-art methods in English, Danish and Swedish.
Moreover, we investigate the semantic features captured by these embeddings
through the proximity of word groupings. We will release these embeddings
publicly to help researchers in the development and enhancement of multilingual
applications.
DeepWalk: Online Learning of Social Representations
Authors: Bryan Perozzi, Rami Al-Rfou, Steven Skiena
Date: 2014-03-26 00:00:00
Tasks: Anomaly Detection, Language Modeling, Language Modelling, Node Classification

We present DeepWalk, a novel approach for learning latent representations of
vertices in a network. These latent representations encode social relations in
a continuous vector space, which is easily exploited by statistical models.
DeepWalk generalizes recent advancements in language modeling and unsupervised
feature learning (or deep learning) from sequences of words to graphs. DeepWalk
uses local information obtained from truncated random walks to learn latent
representations by treating walks as the equivalent of sentences. We
demonstrate DeepWalk's latent representations on several multi-label network
classification tasks for social networks such as BlogCatalog, Flickr, and
YouTube. Our results show that DeepWalk outperforms challenging baselines which
are allowed a global view of the network, especially in the presence of missing
information. DeepWalk's representations can provide $F_1$ scores up to 10%
higher than competing methods when labeled data is sparse. In some experiments,
DeepWalk's representations are able to outperform all baseline methods while
using 60% less training data. DeepWalk is also scalable. It is an online
learning algorithm which builds useful incremental results, and is trivially
parallelizable. These qualities make it suitable for a broad class of real
world applications such as network classification, and anomaly detection.
Performance Limits of Dictionary Learning for Sparse Coding
Authors: Alexander Jung, Yonina C. Eldar, Norbert Görtz
Date: 2014-02-17 00:00:00
Tasks: Dictionary Learning

We consider the problem of dictionary learning under the assumption that the
observed signals can be represented as sparse linear combinations of the
columns of a single large dictionary matrix. In particular, we analyze the
minimax risk of the dictionary learning problem which governs the mean squared
error (MSE) performance of any learning scheme, regardless of its computational
complexity. By following an established information-theoretic method based on
Fanos inequality, we derive a lower bound on the minimax risk for a given
dictionary learning problem. This lower bound yields a characterization of the
sample-complexity, i.e., a lower bound on the required number of observations
such that consistent dictionary learning schemes exist. Our bounds may be
compared with the performance of a given learning scheme, allowing to
characterize how far the method is from optimal performance.
Optimal Population Codes for Control and Estimation
Authors: Alex Susemihl, Ron Meir, Manfred Opper
Date: 2014-06-27 00:00:00

Agents acting in the natural world aim at selecting appropriate actions based
on noisy and partial sensory observations. Many behaviors leading to decision
mak- ing and action selection in a closed loop setting are naturally phrased
within a control theoretic framework. Within the framework of optimal Control
Theory, one is usually given a cost function which is minimized by selecting a
control law based on the observations. While in standard control settings the
sensors are assumed fixed, biological systems often gain from the extra
flexibility of optimiz- ing the sensors themselves. However, this sensory
adaptation is geared towards control rather than perception, as is often
assumed. In this work we show that sen- sory adaptation for control differs
from sensory adaptation for perception, even for simple control setups. This
implies, consistently with recent experimental results, that when studying
sensory adaptation, it is essential to account for the task being performed.
An improved computer vision method for detecting white blood cells
Authors: Erik Cuevas, Margarita Diaz, Miguel Manzanares, Daniel Zaldivar, Marco Perez
Date: 2014-06-26 00:00:00

The automatic detection of White Blood Cells (WBC) still remains as an
unsolved issue in medical imaging. The analysis of WBC images has engaged
researchers from fields of medicine and computer vision alike. Since WBC can be
approximated by an ellipsoid form, an ellipse detector algorithm may be
successfully applied in order to recognize them. This paper presents an
algorithm for the automatic detection of WBC embedded into complicated and
cluttered smear images that considers the complete process as a multi-ellipse
detection problem. The approach, based on the Differential Evolution (DE)
algorithm, transforms the detection task into an optimization problem where
individuals emulate candidate ellipses. An objective function evaluates if such
candidate ellipses are really present in the edge image of the smear. Guided by
the values of such function, the set of encoded candidate ellipses
(individuals) are evolved using the DE algorithm so that they can fit into the
WBC enclosed within the edge-only map of the image. Experimental results from
white blood cell images with a varying range of complexity are included to
validate the efficiency of the proposed technique in terms of accuracy and
robustness.
On a new formulation of nonlocal image filters involving the relative rearrangement
Authors: Gonzalo Galiano, Julián Velasco
Date: 2014-06-27 00:00:00
Tasks: Denoising, Image Denoising

Nonlocal filters are simple and powerful techniques for image denoising. In
this paper we study the reformulation of a broad class of nonlocal filters in
terms of two functional rearrangements: the decreasing and the relative
rearrangements.
  Independently of the dimension of the image, we reformulate these filters as
integral operators defined in a one-dimensional space corresponding to the
level sets measures.
  We prove the equivalency between the original and the rearranged versions of
the filters and propose a discretization in terms of constant-wise
interpolators, which we prove to be convergent to the solution of the
continuous setting.
  For some particular cases, this new formulation allows us to perform a
detailed analysis of the filtering properties. Among others, we prove that the
filtered image is a contrast change of the original image, and that the
filtering procedure behaves asymptotically as a shock filter combined with a
border diffusive term, responsible for the staircaising effect and the loss of
contrast.
Template Matching based Object Detection Using HOG Feature Pyramid
Authors: Anish Acharya
Date: 2014-06-27 00:00:00
Tasks: Object, object-detection, Object Detection, Template Matching

This article provides a step by step development of designing a Object
Detection scheme using the HOG based Feature Pyramid aligned with the concept
of Template Matching.
3D planar patch extraction from stereo using probabilistic region growing
Authors: Vasileios Zografos
Date: 2014-06-27 00:00:00
Tasks: Robot Navigation, Segmentation

This article presents a novel 3D planar patch extraction method using a
probabilistic region growing algorithm. Our method works by simultaneously
initiating multiple planar patches from seed points, the latter determined by
an intensity-based 2D segmentation algorithm in the stereo-pair images. The
patches are grown incrementally and in parallel as 3D scene points are
considered for membership, using a probabilistic distance likelihood measure.
In addition, we have incorporated prior information based on the noise model in
the 2D images and the scene configuration but also include the intensity
information resulting from the initial segmentation. This method works well
across many different data-sets, involving real and synthetic examples of both
regularly and non-regularly sampled data, and is fast enough that may be used
for robot navigation tasks of path detection and obstacle avoidance.
Adaptive texture energy measure method
Authors: Omer Faruk Ertugrul
Date: 2014-06-27 00:00:00
Tasks: Texture Classification

Recent developments in image quality, data storage, and computational
capacity have heightened the need for texture analysis in image process. To
date various methods have been developed and introduced for assessing textures
in images. One of the most popular texture analysis methods is the Texture
Energy Measure (TEM) and it has been used for detecting edges, levels, waves,
spots and ripples by employing predefined TEM masks to images. Despite several
success- ful studies, TEM has a number of serious weaknesses in use. The major
drawback is; the masks are predefined therefore they cannot be adapted to
image. A new method, Adaptive Texture Energy Measure Method (aTEM), was offered
to over- come this disadvantage of TEM by using adaptive masks by adjusting the
contrast, sharpening and orientation angle of the mask. To assess the
applicability of aTEM, it is compared with TEM. The accuracy of the
classification of butterfly, flower seed and Brodatz datasets are 0.08, 0.3292
and 0.3343, respectively by TEM and 0.0053, 0.2417 and 0.3153, respectively by
aTEM. The results of this study indicate that aTEM is a successful method for
texture analysis.
Adaptive Mesh Representation and Restoration of Biomedical Images
Authors: Ke Liu, Ming Xu, Zeyun Yu
Date: 2014-06-27 00:00:00
Tasks: Image Restoration

The triangulation of images has become an active research area in recent
years for its compressive representation and ease of image processing and
visualization. However, little work has been done on how to faithfully recover
image intensities from a triangulated mesh of an image, a process also known as
image restoration or decoding from meshes. The existing methods such as linear
interpolation, least-square interpolation, or interpolation based on radial
basis functions (RBFs) work to some extent, but often yield blurred features
(edges, corners, etc.). The main reason for this problem is due to the
isotropically-defined Euclidean distance that is taken into consideration in
these methods, without considering the anisotropicity of feature intensities in
an image. Moreover, most existing methods use intensities defined at mesh nodes
whose intensities are often ambiguously defined on or near image edges (or
feature boundaries). In the current paper, a new method of restoring an image
from its triangulation representation is proposed, by utilizing anisotropic
radial basis functions (ARBFs). This method considers not only the geometrical
(Euclidean) distances but also the local feature orientations (anisotropic
intensities). Additionally, this method is based on the intensities of mesh
faces instead of mesh nodes and thus provides a more robust restoration. The
two strategies together guarantee excellent feature-preserving restoration of
an image with arbitrary super-resolutions from its triangulation
representation, as demonstrated by various experiments provided in the paper.
Architecture of a Web-based Predictive Editor for Controlled Natural Language Processing
Authors: Stephen Guy, Rolf Schwitter
Date: 2014-06-27 00:00:00
Tasks: Sentence

In this paper, we describe the architecture of a web-based predictive text
editor being developed for the controlled natural language PENG$^{ASP)$. This
controlled language can be used to write non-monotonic specifications that have
the same expressive power as Answer Set Programs. In order to support the
writing process of these specifications, the predictive text editor
communicates asynchronously with the controlled natural language processor that
generates lookahead categories and additional auxiliary information for the
author of a specification text. The text editor can display multiple sets of
lookahead categories simultaneously for different possible sentence
completions, anaphoric expressions, and supports the addition of new content
words to the lexicon.
Generalized Canonical Correlation Analysis for Classification
Authors: Cencheng Shen, Ming Sun, Minh Tang, Carey E. Priebe
Date: 2013-04-30 00:00:00
Tasks: Classification, General Classification

For multiple multivariate data sets, we derive conditions under which
Generalized Canonical Correlation Analysis (GCCA) improves classification
performance of the projected datasets, compared to standard Canonical
Correlation Analysis (CCA) using only two data sets. We illustrate our
theoretical results with simulations and a real data experiment.
Communicating and resolving entity references
Authors: R. V. Guha
Date: 2014-06-26 00:00:00

Statements about entities occur everywhere, from newspapers and web pages to
structured databases. Correlating references to entities across systems that
use different identifiers or names for them is a widespread problem. In this
paper, we show how shared knowledge between systems can be used to solve this
problem. We present "reference by description", a formal model for resolving
references. We provide some results on the conditions under which a randomly
chosen entity in one system can, with high probability, be mapped to the same
entity in a different system.
Deep Learning Multi-View Representation for Face Recognition
Authors: Zhenyao Zhu, Ping Luo, Xiaogang Wang, Xiaoou Tang
Date: 2014-06-26 00:00:00
Tasks: Deep Learning, Face Recognition

Various factors, such as identities, views (poses), and illuminations, are coupled in face images. Disentangling the identity and view representations is a major challenge in face recognition. Existing face recognition systems either use handcrafted features or learn features discriminatively to improve recognition accuracy. This is different from the behavior of human brain. Intriguingly, even without accessing 3D data, human not only can recognize face identity, but can also imagine face images of a person under different viewpoints given a single 2D image, making face perception in the brain robust to view changes. In this sense, human brain has learned and encoded 3D face models from 2D images. To take into account this instinct, this paper proposes a novel deep neural net, named multi-view perceptron (MVP), which can untangle the identity and view features, and infer a full spectrum of multi-view images in the meanwhile, given a single 2D face image. The identity features of MVP achieve superior performance on the MultiPIE dataset. MVP is also capable to interpolate and predict images under viewpoints that are unobserved in the training data.
Edge Label Inference in Generalized Stochastic Block Models: from Spectral Theory to Impossibility Results
Authors: Jiaming Xu, Laurent Massoulié, Marc Lelarge
Date: 2014-06-26 00:00:00
Tasks: Community Detection

The classical setting of community detection consists of networks exhibiting
a clustered structure. To more accurately model real systems we consider a
class of networks (i) whose edges may carry labels and (ii) which may lack a
clustered structure. Specifically we assume that nodes possess latent
attributes drawn from a general compact space and edges between two nodes are
randomly generated and labeled according to some unknown distribution as a
function of their latent attributes. Our goal is then to infer the edge label
distributions from a partially observed network. We propose a computationally
efficient spectral algorithm and show it allows for asymptotically correct
inference when the average node degree could be as low as logarithmic in the
total number of nodes. Conversely, if the average node degree is below a
specific constant threshold, we show that no algorithm can achieve better
inference than guessing without using the observations. As a byproduct of our
analysis, we show that our model provides a general procedure to construct
random graph models with a spectrum asymptotic to a pre-specified eigenvalue
distribution such as a power-law distribution.