pwc-archive/papers-with-abstracts huggingface dataset:
Tyler's Covariance Matrix Estimator in Elliptical Models with Convex Structure
Authors: Ilya Soloveychik, Ami Wiesel
Date: 2014-04-07 00:00:00

We address structured covariance estimation in elliptical distributions by
assuming that the covariance is a priori known to belong to a given convex set,
e.g., the set of Toeplitz or banded matrices. We consider the General Method of
Moments (GMM) optimization applied to robust Tyler's scatter M-estimator
subject to these convex constraints. Unfortunately, GMM turns out to be
non-convex due to the objective. Instead, we propose a new COCA estimator - a
convex relaxation which can be efficiently solved. We prove that the relaxation
is tight in the unconstrained case for a finite number of samples, and in the
constrained case asymptotically. We then illustrate the advantages of COCA in
synthetic simulations with structured compound Gaussian distributions. In these
examples, COCA outperforms competing methods such as Tyler's estimator and its
projection onto the structure set.
Intelligent Indoor Mobile Robot Navigation Using Stereo Vision
Authors: Arjun B. Krishnan, Jayaram Kollipara
Date: 2014-09-10 00:00:00
Tasks: Navigate, Robot Navigation

Majority of the existing robot navigation systems, which facilitate the use
of laser range finders, sonar sensors or artificial landmarks, has the ability
to locate itself in an unknown environment and then build a map of the
corresponding environment. Stereo vision, while still being a rapidly
developing technique in the field of autonomous mobile robots, are currently
less preferable due to its high implementation cost. This paper aims at
describing an experimental approach for the building of a stereo vision system
that helps the robots to avoid obstacles and navigate through indoor
environments and at the same time remaining very much cost effective. This
paper discusses the fusion techniques of stereo vision and ultrasound sensors
which helps in the successful navigation through different types of complex
environments. The data from the sensor enables the robot to create the two
dimensional topological map of unknown environments and stereo vision systems
models the three dimension model of the same environment.
Word Sense Disambiguation using WSD specific Wordnet of Polysemy Words
Authors: Udaya Raj Dhungana, Subarna Shakya, Kabita Baral, Bharat Sharma
Date: 2014-09-10 00:00:00
Tasks: Word Sense Disambiguation

This paper presents a new model of WordNet that is used to disambiguate the
correct sense of polysemy word based on the clue words. The related words for
each sense of a polysemy word as well as single sense word are referred to as
the clue words. The conventional WordNet organizes nouns, verbs, adjectives and
adverbs together into sets of synonyms called synsets each expressing a
different concept. In contrast to the structure of WordNet, we developed a new
model of WordNet that organizes the different senses of polysemy words as well
as the single sense words based on the clue words. These clue words for each
sense of a polysemy word as well as for single sense word are used to
disambiguate the correct meaning of the polysemy word in the given context
using knowledge based Word Sense Disambiguation (WSD) algorithms. The clue word
can be a noun, verb, adjective or adverb.
Metric Learning for Temporal Sequence Alignment
Authors: Damien Garreau, Rémi Lajugie, Sylvain Arlot, Francis Bach
Date: 2014-09-10 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: feature selection, Metric Learning, Structured Prediction, Time Series, Time Series Analysis

In this paper, we propose to learn a Mahalanobis distance to perform
alignment of multivariate time series. The learning examples for this task are
time series for which the true alignment is known. We cast the alignment
problem as a structured prediction task, and propose realistic losses between
alignments for which the optimization is tractable. We provide experiments on
real data in the audio to audio context, where we show that the learning of a
similarity measure leads to improvements in the performance of the alignment
task. We also propose to use this metric learning framework to perform feature
selection and, from basic audio features, build a combination of these with
better performance for the alignment.
An improved genetic algorithm with a local optimization strategy and an extra mutation level for solving traveling salesman problem
Authors: Keivan Borna, Vahid Haji Hashemi
Date: 2014-09-10 00:00:00
Tasks: Traveling Salesman Problem

The Traveling salesman problem (TSP) is proved to be NP-complete in most
cases. The genetic algorithm (GA) is one of the most useful algorithms for
solving this problem. In this paper a conventional GA is compared with an
improved hybrid GA in solving TSP. The improved or hybrid GA consist of
conventional GA and two local optimization strategies. The first strategy is
extracting all sequential groups including four cities of samples and changing
the two central cities with each other. The second local optimization strategy
is similar to an extra mutation process. In this step with a low probability a
sample is selected. In this sample two random cities are defined and the path
between these cities is reversed. The computation results show that the
proposed method also finds better paths than the conventional GA within an
acceptable computation time.
Image Denoising using New Adaptive Based Median Filters
Authors: Suman Shrestha
Date: 2014-09-10 00:00:00
Tasks: Denoising, Image Denoising

Noise is a major issue while transferring images through all kinds of
electronic communication. One of the most common noise in electronic
communication is an impulse noise which is caused by unstable voltage. In this
paper, the comparison of known image denoising techniques is discussed and a
new technique using the decision based approach has been used for the removal
of impulse noise. All these methods can primarily preserve image details while
suppressing impulsive noise. The principle of these techniques is at first
introduced and then analysed with various simulation results using MATLAB. Most
of the previously known techniques are applicable for the denoising of images
corrupted with less noise density. Here a new decision based technique has been
presented which shows better performances than those already being used. The
comparisons are made based on visual appreciation and further quantitatively by
Mean Square error (MSE) and Peak Signal to Noise Ratio (PSNR) of different
filtered images..
One-Dimensional Vector based Pattern Matching
Authors: Y. M. Fouda
Date: 2014-09-10 00:00:00
Tasks: Template Matching

Template matching is a basic method in image analysis to extract useful
information from images. In this paper, we suggest a new method for pattern
matching. Our method transform the template image from two dimensional image
into one dimensional vector. Also all sub-windows (same size of template) in
the reference image will transform into one dimensional vectors. The three
similarity measures SAD, SSD, and Euclidean are used to compute the likeness
between template and all sub-windows in the reference image to find the best
match. The experimental results show the superior performance of the proposed
method over the conventional methods on various template of different sizes.
A Study of Association Measures and their Combination for Arabic MWT Extraction
Authors: Abdelkader El Mahdaouy, Saïd EL Alaoui Ouatik, Eric Gaussier
Date: 2014-09-10 00:00:00
Tasks: Information Retrieval, Question Answering, Retrieval, Term Extraction, Text Categorization

Automatic Multi-Word Term (MWT) extraction is a very important issue to many
applications, such as information retrieval, question answering, and text
categorization. Although many methods have been used for MWT extraction in
English and other European languages, few studies have been applied to Arabic.
In this paper, we propose a novel, hybrid method which combines linguistic and
statistical approaches for Arabic Multi-Word Term extraction. The main
contribution of our method is to consider contextual information and both
termhood and unithood for association measures at the statistical filtering
step. In addition, our technique takes into account the problem of MWT
variation in the linguistic filtering step. The performance of the proposed
statistical measure (NLC-value) is evaluated using an Arabic environment corpus
by comparing it with some existing competitors. Experimental results show that
our NLC-value measure outperforms the other ones in term of precision for both
bi-grams and tri-grams.
Scalable Bayesian Modelling of Paired Symbols
Authors: Ulrich Paquet, Noam Koenigstein, Ole Winther
Date: 2014-09-09 00:00:00

We present a novel, scalable and Bayesian approach to modelling the
occurrence of pairs of symbols (i,j) drawn from a large vocabulary. Observed
pairs are assumed to be generated by a simple popularity based selection
process followed by censoring using a preference function. By basing inference
on the well-founded principle of variational bounding, and using new
site-independent bounds, we show how a scalable inference procedure can be
obtained for large data sets. State of the art results are presented on
real-world movie viewing data.
"Look Ma, No Hands!" A Parameter-Free Topic Model
Authors: Jian Tang, Ming Zhang, Qiaozhu Mei
Date: 2014-09-10 00:00:00
Tasks: Diversity, Model Selection, Topic Models

It has always been a burden to the users of statistical topic models to
predetermine the right number of topics, which is a key parameter of most topic
models. Conventionally, automatic selection of this parameter is done through
either statistical model selection (e.g., cross-validation, AIC, or BIC) or
Bayesian nonparametric models (e.g., hierarchical Dirichlet process). These
methods either rely on repeated runs of the inference algorithm to search
through a large range of parameter values which does not suit the mining of big
data, or replace this parameter with alternative parameters that are less
intuitive and still hard to be determined. In this paper, we explore to
"eliminate" this parameter from a new perspective. We first present a
nonparametric treatment of the PLSA model named nonparametric probabilistic
latent semantic analysis (nPLSA). The inference procedure of nPLSA allows for
the exploration and comparison of different numbers of topics within a single
execution, yet remains as simple as that of PLSA. This is achieved by
substituting the parameter of the number of topics with an alternative
parameter that is the minimal goodness of fit of a document. We show that the
new parameter can be further eliminated by two parameter-free treatments:
either by monitoring the diversity among the discovered topics or by a weak
supervision from users in the form of an exemplar topic. The parameter-free
topic model finds the appropriate number of topics when the diversity among the
discovered topics is maximized, or when the granularity of the discovered
topics matches the exemplar topic. Experiments on both synthetic and real data
prove that the parameter-free topic model extracts topics with a comparable
quality comparing to classical topic models with "manual transmission". The
quality of the topics outperforms those extracted through classical Bayesian
nonparametric models.
Quantum Edge Detection for Image Segmentation in Optical Environments
Authors: Mario Mastriani
Date: 2014-09-09 00:00:00
Tasks: Edge Detection, Image Segmentation, Semantic Segmentation, valid

A quantum edge detector for image segmentation in optical environments is
presented in this work. A Boolean version of the same detector is presented
too. The quantum version of the new edge detector works with computational
basis states, exclusively. This way, we can easily avoid the problem of quantum
measurement retrieving the result of applying the new detector on the image.
Besides, a new criterion and logic based on projections onto vertical axis of
Bloch's Sphere exclusively are presented too. This approach will allow us: 1) a
simpler development of logic quantum operations, where they will closer to
those used in the classical logic operations, 2) building simple and robust
classical-to-quantum and quantum-to-classical interfaces. Said so far is
extended to quantum algorithms outside image processing too. In a special
section on metric and simulations, a new metric based on the comparison between
the classical and quantum versions algorithms for edge detection of images is
presented. Notable differences between the results of classical and quantum
versions of such algorithms (outside and inside of quantum computer,
respectively) show the existence of implementation problems involved in the
experiment, and that they have not been properly modeled for optical
environments. However, although they are different, the quantum results are
equally valid. The latter is clearly seen in the computer simulations
Non-Convex Boosting Overcomes Random Label Noise
Authors: Sunsern Cheamanunkul, Evan Ettinger, Yoav Freund
Date: 2014-09-09 00:00:00
Tasks: Sensitivity

The sensitivity of Adaboost to random label noise is a well-studied problem.
LogitBoost, BrownBoost and RobustBoost are boosting algorithms claimed to be
less sensitive to noise than AdaBoost. We present the results of experiments
evaluating these algorithms on both synthetic and real datasets. We compare the
performance on each of datasets when the labels are corrupted by different
levels of independent label noise. In presence of random label noise, we found
that BrownBoost and RobustBoost perform significantly better than AdaBoost and
LogitBoost, while the difference between each pair of algorithms is
insignificant. We provide an explanation for the difference based on the margin
distributions of the algorithms.
Co-adaptation in a Handwriting Recognition System
Authors: Sunsern Cheamanunkul, Yoav Freund
Date: 2014-09-09 00:00:00
Tasks: Handwriting Recognition

Handwriting is a natural and versatile method for human-computer interaction,
especially on small mobile devices such as smart phones. However, as
handwriting varies significantly from person to person, it is difficult to
design handwriting recognizers that perform well for all users. A natural
solution is to use machine learning to adapt the recognizer to the user. One
complicating factor is that, as the computer adapts to the user, the user also
adapts to the computer and probably changes their handwriting. This paper
investigates the dynamics of co-adaptation, a process in which both the
computer and the user are adapting their behaviors in order to improve the
speed and accuracy of the communication through handwriting. We devised an
information-theoretic framework for quantifying the efficiency of a handwriting
system where the system includes both the user and the computer. Using this
framework, we analyzed data collected from an adaptive handwriting recognition
system and characterized the impact of machine adaptation and of human
adaptation. We found that both machine adaptation and human adaptation have
significant impact on the input rate and must be considered together in order
to improve the efficiency of the system as a whole.
Ambiguity-Driven Fuzzy C-Means Clustering: How to Detect Uncertain Clustered Records
Authors: Meysam Ghaffari, Nasser Ghadiri
Date: 2014-09-09 00:00:00
Tasks: Clustering, Decision Making, feature selection, Medical Diagnosis

As a well-known clustering algorithm, Fuzzy C-Means (FCM) allows each input
sample to belong to more than one cluster, providing more flexibility than
non-fuzzy clustering methods. However, the accuracy of FCM is subject to false
detections caused by noisy records, weak feature selection and low certainty of
the algorithm in some cases. The false detections are very important in some
decision-making application domains like network security and medical
diagnosis, where weak decisions based on such false detections may lead to
catastrophic outcomes. They are mainly emerged from making decisions about a
subset of records that do not provide enough evidence to make a good decision.
In this paper, we propose a method for detecting such ambiguous records in FCM
by introducing a certainty factor to decrease invalid detections. This approach
enables us to send the detected ambiguous records to another discrimination
method for a deeper investigation, thus increasing the accuracy by lowering the
error rate. Most of the records are still processed quickly and with low error
rate which prevents performance loss compared to similar hybrid methods.
Experimental results of applying the proposed method on several datasets from
different domains show a significant decrease in error rate as well as improved
sensitivity of the algorithm.
Enforcing Label and Intensity Consistency for IR Target Detection
Authors: Toufiq Parag
Date: 2014-09-09 00:00:00
Tasks: Binary Classification

This study formulates the IR target detection as a binary classification
problem of each pixel. Each pixel is associated with a label which indicates
whether it is a target or background pixel. The optimal label set for all the
pixels of an image maximizes aposteriori distribution of label configuration
given the pixel intensities. The posterior probability is factored into (or
proportional to) a conditional likelihood of the intensity values and a prior
probability of label configuration. Each of these two probabilities are
computed assuming a Markov Random Field (MRF) on both pixel intensities and
their labels. In particular, this study enforces neighborhood dependency on
both intensity values, by a Simultaneous Auto Regressive (SAR) model, and on
labels, by an Auto-Logistic model. The parameters of these MRF models are
learned from labeled examples. During testing, an MRF inference technique,
namely Iterated Conditional Mode (ICM), produces the optimal label for each
pixel. The detection performance is further improved by incorporating temporal
information through background subtraction. High performances on benchmark
datasets demonstrate effectiveness of this method for IR target detection.
Short-term plasticity as cause-effect hypothesis testing in distal reward learning
Authors: Andrea Soltoggio
Date: 2014-02-04 00:00:00
Tasks: Two-sample testing

Asynchrony, overlaps and delays in sensory-motor signals introduce ambiguity
as to which stimuli, actions, and rewards are causally related. Only the
repetition of reward episodes helps distinguish true cause-effect relationships
from coincidental occurrences. In the model proposed here, a novel plasticity
rule employs short and long-term changes to evaluate hypotheses on cause-effect
relationships. Transient weights represent hypotheses that are consolidated in
long-term memory only when they consistently predict or cause future rewards.
The main objective of the model is to preserve existing network topologies when
learning with ambiguous information flows. Learning is also improved by biasing
the exploration of the stimulus-response space towards actions that in the past
occurred before rewards. The model indicates under which conditions beliefs can
be consolidated in long-term memory, it suggests a solution to the
plasticity-stability dilemma, and proposes an interpretation of the role of
short-term plasticity.
Automatic Dimension Selection for a Non-negative Factorization Approach to Clustering Multiple Random Graphs
Authors: Nam H. Lee, I-Jeng Wang, Youngser Park, Care E. Priebe, Michael Rosen
Date: 2014-06-24 00:00:00
Tasks: Clustering, Model Selection

We consider a problem of grouping multiple graphs into several clusters using
singular value thesholding and non-negative factorization. We derive a model
selection information criterion to estimate the number of clusters. We
demonstrate our approach using "Swimmer data set" as well as simulated data
set, and compare its performance with two standard clustering algorithms.
Context-specific independence in graphical log-linear models
Authors: Henrik Nyman, Johan Pensar, Timo Koski, Jukka Corander
Date: 2014-09-09 00:00:00
Tasks: parameter estimation

Log-linear models are the popular workhorses of analyzing contingency tables.
A log-linear parameterization of an interaction model can be more expressive
than a direct parameterization based on probabilities, leading to a powerful
way of defining restrictions derived from marginal, conditional and
context-specific independence. However, parameter estimation is often simpler
under a direct parameterization, provided that the model enjoys certain
decomposability properties. Here we introduce a cyclical projection algorithm
for obtaining maximum likelihood estimates of log-linear parameters under an
arbitrary context-specific graphical log-linear model, which needs not satisfy
criteria of decomposability. We illustrate that lifting the restriction of
decomposability makes the models more expressive, such that additional
context-specific independencies embedded in real data can be identified. It is
also shown how a context-specific graphical model can correspond to a
non-hierarchical log-linear parameterization with a concise interpretation.
This observation can pave way to further development of non-hierarchical
log-linear models, which have been largely neglected due to their believed lack
of interpretability.
eAnt-Miner : An Ensemble Ant-Miner to Improve the ACO Classification
Authors: Gopinath Chennupati
Date: 2014-09-09 00:00:00
Tasks: Classification, General Classification

Ant Colony Optimization (ACO) has been applied in supervised learning in
order to induce classification rules as well as decision trees, named
Ant-Miners. Although these are competitive classifiers, the stability of these
classifiers is an important concern that owes to their stochastic nature. In
this paper, to address this issue, an acclaimed machine learning technique
named, ensemble of classifiers is applied, where an ACO classifier is used as a
base classifier to prepare the ensemble. The main trade-off is, the predictions
in the new approach are determined by discovering a group of models as opposed
to the single model classification. In essence, we prepare multiple models from
the randomly replaced samples of training data from which, a unique model is
prepared by aggregating the models to test the unseen data points. The main
objective of this new approach is to increase the stability of the Ant-Miner
results there by improving the performance of ACO classification. We found that
the ensemble Ant-Miners significantly improved the stability by reducing the
classification error on unseen data.
F-formation Detection: Individuating Free-standing Conversational Groups in Images
Authors: Francesco Setti, Chris Russell, Chiara Bassetti, Marco Cristani
Date: 2014-09-09 00:00:00
Tasks: Clustering

Detection of groups of interacting people is a very interesting and useful
task in many modern technologies, with application fields spanning from
video-surveillance to social robotics. In this paper we first furnish a
rigorous definition of group considering the background of the social sciences:
this allows us to specify many kinds of group, so far neglected in the Computer
Vision literature. On top of this taxonomy, we present a detailed state of the
art on the group detection algorithms. Then, as a main contribution, we present
a brand new method for the automatic detection of groups in still images, which
is based on a graph-cuts framework for clustering individuals; in particular we
are able to codify in a computational sense the sociological definition of
F-formation, that is very useful to encode a group having only proxemic
information: position and orientation of people. We call the proposed method
Graph-Cuts for F-formation (GCFF). We show how GCFF definitely outperforms all
the state of the art methods in terms of different accuracy measures (some of
them are brand new), demonstrating also a strong robustness to noise and
versatility in recognizing groups of various cardinality.