pwc-archive/papers-with-abstracts huggingface dataset:
Sparse CCA via Precision Adjusted Iterative Thresholding
Authors: Mengjie Chen, Chao GAO, Zhao Ren, Harrison H. Zhou
Date: 2013-11-24 00:00:00
Tasks: Prognosis

Sparse Canonical Correlation Analysis (CCA) has received considerable
attention in high-dimensional data analysis to study the relationship between
two sets of random variables. However, there has been remarkably little
theoretical statistical foundation on sparse CCA in high-dimensional settings
despite active methodological and applied research activities. In this paper,
we introduce an elementary sufficient and necessary characterization such that
the solution of CCA is indeed sparse, propose a computationally efficient
procedure, called CAPIT, to estimate the canonical directions, and show that
the procedure is rate-optimal under various assumptions on nuisance parameters.
The procedure is applied to a breast cancer dataset from The Cancer Genome
Atlas project. We identify methylation probes that are associated with genes,
which have been previously characterized as prognosis signatures of the
metastasis of breast cancer.
Robust Low-rank Tensor Recovery: Models and Algorithms
Authors: Donald Goldfarb, Zhiwei Qin
Date: 2013-11-24 00:00:00
Tasks: Missing Values

Robust tensor recovery plays an instrumental role in robustifying tensor
decompositions for multilinear data analysis against outliers, gross
corruptions and missing values and has a diverse array of applications. In this
paper, we study the problem of robust low-rank tensor recovery in a convex
optimization framework, drawing upon recent advances in robust Principal
Component Analysis and tensor completion. We propose tailored optimization
algorithms with global convergence guarantees for solving both the constrained
and the Lagrangian formulations of the problem. These algorithms are based on
the highly efficient alternating direction augmented Lagrangian and accelerated
proximal gradient methods. We also propose a nonconvex model that can often
improve the recovery results from the convex models. We investigate the
empirical recoverability properties of the convex and nonconvex formulations
and compare the computational performance of the algorithms on simulated data.
We demonstrate through a number of real applications the practical
effectiveness of this convex optimization framework for robust low-rank tensor
recovery.
Ranking and combining multiple predictors without labeled data
Authors: Fabio Parisi, Francesco Strino, Boaz Nadler, Yuval Kluger
Date: 2013-03-13 00:00:00
Tasks: Decision Making

In a broad range of classification and decision making problems, one is given
the advice or predictions of several classifiers, of unknown reliability, over
multiple questions or queries. This scenario is different from the standard
supervised setting, where each classifier accuracy can be assessed using
available labeled data, and raises two questions: given only the predictions of
several classifiers over a large set of unlabeled test data, is it possible to
a) reliably rank them; and b) construct a meta-classifier more accurate than
most classifiers in the ensemble? Here we present a novel spectral approach to
address these questions. First, assuming conditional independence between
classifiers, we show that the off-diagonal entries of their covariance matrix
correspond to a rank-one matrix. Moreover, the classifiers can be ranked using
the leading eigenvector of this covariance matrix, as its entries are
proportional to their balanced accuracies. Second, via a linear approximation
to the maximum likelihood estimator, we derive the Spectral Meta-Learner (SML),
a novel ensemble classifier whose weights are equal to this eigenvector
entries. On both simulated and real data, SML typically achieves a higher
accuracy than most classifiers in the ensemble and can provide a better
starting point than majority voting, for estimating the maximum likelihood
solution. Furthermore, SML is robust to the presence of small malicious groups
of classifiers designed to veer the ensemble prediction away from the (unknown)
ground truth.
David Poole's Specificity Revised
Authors: Claus-Peter Wirth, Frieder Stolzenburg
Date: 2013-08-22 00:00:00
Tasks: Relation, Specificity

In the middle of the 1980s, David Poole introduced a semantical,
model-theoretic notion of specificity to the artificial-intelligence community.
Since then it has found further applications in non-monotonic reasoning, in
particular in defeasible reasoning. Poole tried to approximate the intuitive
human concept of specificity, which seems to be essential for reasoning in
everyday life with its partial and inconsistent information. His notion,
however, turns out to be intricate and problematic, which --- as we show ---
can be overcome to some extent by a closer approximation of the intuitive human
concept of specificity. Besides the intuitive advantages of our novel
specificity ordering over Poole's specificity relation in the classical
examples of the literature, we also report some hard mathematical facts:
Contrary to what was claimed before, we show that Poole's relation is not
transitive. The present means to decide our novel specificity relation,
however, show only a slight improvement over the known ones for Poole's
relation, and further work is needed in this aspect.
Detection of Partially Visible Objects
Authors: Patrick Ott, Mark Everingham, Jiri Matas
Date: 2013-11-24 00:00:00
Tasks: Object, object-detection, Object Detection

An "elephant in the room" for most current object detection and localization
methods is the lack of explicit modelling of partial visibility due to
occlusion by other objects or truncation by the image boundary. Based on a
sliding window approach, we propose a detection method which explicitly models
partial visibility by treating it as a latent variable. A novel non-maximum
suppression scheme is proposed which takes into account the inferred partial
visibility of objects while providing a globally optimal solution. The method
gives more detailed scene interpretations than conventional detectors in that
we are able to identify the visible parts of an object. We report improved
average precision on the PASCAL VOC 2010 dataset compared to a baseline
detector.
Q-learning optimization in a multi-agents system for image segmentation
Authors: Issam Qaffou, Mohamed Sadgal, Abdelaziz Elfazziki
Date: 2013-11-23 00:00:00
Tasks: Image Segmentation, Q-Learning, Semantic Segmentation

To know which operators to apply and in which order, as well as attributing
good values to their parameters is a challenge for users of computer vision.
This paper proposes a solution to this problem as a multi-agent system modeled
according to the Vowel approach and using the Q-learning algorithm to optimize
its choice. An implementation is given to test and validate this method.
Skin Texture Recognition Using Neural Networks
Authors: Nidhal K. El Abbadi, Nazar Dahir, Zaid Abd Alkareem
Date: 2013-11-23 00:00:00
Tasks: Face Detection

Skin recognition is used in many applications ranging from algorithms for
face detection, hand gesture analysis, and to objectionable image filtering. In
this work a skin recognition system was developed and tested. While many skin
segmentation algorithms relay on skin color, our work relies on both skin color
and texture features (features derives from the GLCM) to give a better and more
efficient recognition accuracy of skin textures. We used feed forward neural
networks to classify input textures images to be skin or non skin textures. The
system gave very encouraging results during the neural network generalization
face.
On the Design and Analysis of Multiple View Descriptors
Authors: Jingming Dong, Jonathan Balzer, Damek Davis, Joshua Hernandez, Stefano Soatto
Date: 2013-11-23 00:00:00
Tasks: Specificity

We propose an extension of popular descriptors based on gradient orientation
histograms (HOG, computed in a single image) to multiple views. It hinges on
interpreting HOG as a conditional density in the space of sampled images, where
the effects of nuisance factors such as viewpoint and illumination are
marginalized. However, such marginalization is performed with respect to a very
coarse approximation of the underlying distribution. Our extension leverages on
the fact that multiple views of the same scene allow separating intrinsic from
nuisance variability, and thus afford better marginalization of the latter. The
result is a descriptor that has the same complexity of single-view HOG, and can
be compared in the same manner, but exploits multiple views to better trade off
insensitivity to nuisance variability with specificity to intrinsic
variability. We also introduce a novel multi-view wide-baseline matching
dataset, consisting of a mixture of real and synthetic objects with ground
truthed camera motion and dense three-dimensional geometry.
Build Electronic Arabic Lexicon
Authors: Nidhal El-Abbadi, Ahmed Nidhal Khdhair, Adel Al-Nasrawi
Date: 2013-11-23 00:00:00

There are many known Arabic lexicons organized on different ways, each of
them has a different number of Arabic words according to its organization way.
This paper has used mathematical relations to count a number of Arabic words,
which proofs the number of Arabic words presented by Al Farahidy. The paper
also presents new way to build an electronic Arabic lexicon by using a hash
function that converts each word (as input) to correspond a unique integer
number (as output), these integer numbers will be used as an index to a lexicon
entry.
Dynamic Model of Facial Expression Recognition based on Eigen-face Approach
Authors: Nikunj Bajaj, Aurobinda Routray, S. L. Happy
Date: 2013-11-23 00:00:00
Tasks: Emotion Recognition, Facial Expression Recognition, Facial Expression Recognition (FER), Marketing

Emotions are best way of communicating information; and sometimes it carry more information than words. Recently, there has been a huge interest in automatic recognition of human emotion because of its wide spread application in security, surveillance, marketing, advertisement, and human-computer interaction. To communicate with a computer in a natural way, it will be desirable to use more natural modes of human communication based on voice, gestures and facial expressions. In this paper, a holistic approach for facial expression recognition is proposed which captures the variation in facial features in temporal domain and classifies the sequence of images in different emotions. The proposed method uses Haar-like features to detect face in an image. The dimensionality of the eigenspace is reduced using Principal Component Analysis (PCA). By projecting the subsequent face images into principal eigen directions, the variation pattern of the obtained weight vector is modeled to classify it into different emotions. Owing to the variations of expressions for different people and its intensity, a person specific method for emotion recognition is followed. Using the gray scale images of the frontal face, the system is able to classify four basic emotions such as happiness, sadness, surprise, and anger.
A brief network analysis of Artificial Intelligence publication
Authors: Yunpeng Li, Jie Liu, Yong Deng
Date: 2013-11-23 00:00:00
Tasks: Articles

In this paper, we present an illustration to the history of Artificial
Intelligence(AI) with a statistical analysis of publish since 1940. We
collected and mined through the IEEE publish data base to analysis the
geological and chronological variance of the activeness of research in AI. The
connections between different institutes are showed. The result shows that the
leading community of AI research are mainly in the USA, China, the Europe and
Japan. The key institutes, authors and the research hotspots are revealed. It
is found that the research institutes in the fields like Data Mining, Computer
Vision, Pattern Recognition and some other fields of Machine Learning are quite
consistent, implying a strong interaction between the community of each field.
It is also showed that the research of Electronic Engineering and Industrial or
Commercial applications are very active in California. Japan is also publishing
a lot of papers in robotics. Due to the limitation of data source, the result
might be overly influenced by the number of published articles, which is to our
best improved by applying network keynode analysis on the research community
instead of merely count the number of publish.
Brain Tumor Detection Based On Symmetry Information
Authors: Narkhede Sachin G, Vaishali Khairnar
Date: 2013-11-23 00:00:00
Tasks: Brain Tumor Segmentation, Segmentation, Tumor Segmentation

Advances in computing technology have allowed researchers across many fields
of endeavor to collect and maintain vast amounts of observational statistical
data such as clinical data, biological patient data, data regarding access of
web sites, financial data, and the like. This paper addresses some of the
challenging issues on brain magnetic resonance (MR) image tumor segmentation
caused by the weak correlation between magnetic resonance imaging (MRI)
intensity and anatomical meaning. With the objective of utilizing more
meaningful information to improve brain tumor segmentation, an approach which
employs bilateral symmetry information as an additional feature for
segmentation is proposed. This is motivated by potential performance
improvement in the general automatic brain tumor segmentation systems which are
important for many medical and scientific applications
Fast Training of Effective Multi-class Boosting Using Coordinate Descent Optimization
Authors: Guosheng Lin, Chunhua Shen, Anton Van Den Hengel, David Suter
Date: 2013-11-23 00:00:00
Tasks: Multi-class Classification

Wepresentanovelcolumngenerationbasedboostingmethod for multi-class
classification. Our multi-class boosting is formulated in a single optimization
problem as in Shen and Hao (2011). Different from most existing multi-class
boosting methods, which use the same set of weak learners for all the classes,
we train class specified weak learners (i.e., each class has a different set of
weak learners). We show that using separate weak learner sets for each class
leads to fast convergence, without introducing additional computational
overhead in the training procedure. To further make the training more efficient
and scalable, we also propose a fast co- ordinate descent method for solving
the optimization problem at each boosting iteration. The proposed coordinate
descent method is conceptually simple and easy to implement in that it is a
closed-form solution for each coordinate update. Experimental results on a
variety of datasets show that, compared to a range of existing multi-class
boosting meth- ods, the proposed method has much faster convergence rate and
better generalization performance in most cases. We also empirically show that
the proposed fast coordinate descent algorithm needs less training time than
the MultiBoost algorithm in Shen and Hao (2011).
Automatic Ranking of MT Outputs using Approximations
Authors: Pooja Gupta, Nisheeth Joshi, Iti Mathur
Date: 2013-11-22 00:00:00
Tasks: Machine Translation, Translation

Since long, research on machine translation has been ongoing. Still, we do
not get good translations from MT engines so developed. Manual ranking of these
outputs tends to be very time consuming and expensive. Identifying which one is
better or worse than the others is a very taxing task. In this paper, we show
an approach which can provide automatic ranks to MT outputs (translations)
taken from different MT Engines and which is based on N-gram approximations. We
provide a solution where no human intervention is required for ranking systems.
Further we also show the evaluations of our results which show equivalent
results as that of human ranking.
Dictionary-Learning-Based Reconstruction Method for Electron Tomography
Authors: Baodong Liu, Hengyong Yu, Scott S. Verbridge, Lizhi Sun, Ge Wang
Date: 2013-11-22 00:00:00
Tasks: Compressive Sensing, Dictionary Learning, Electron Tomography

Electron tomography usually suffers from so called missing wedge artifacts
caused by limited tilt angle range. An equally sloped tomography (EST)
acquisition scheme (which should be called the linogram sampling scheme) was
recently applied to achieve 2.4-angstrom resolution. On the other hand, a
compressive sensing-inspired reconstruction algorithm, known as adaptive
dictionary based statistical iterative reconstruction (ADSIR), has been
reported for x-ray computed tomography. In this paper, we evaluate the EST,
ADSIR and an ordered-subset simultaneous algebraic reconstruction technique
(OS-SART), and compare the ES and equally angled (EA) data acquisition modes.
Our results show that OS-SART is comparable to EST, and the ADSIR outperforms
EST and OS-SART. Furthermore, the equally sloped projection data acquisition
mode has no advantage over the conventional equally angled mode in the context.
Automated and Weighted Self-Organizing Time Maps
Authors: Peter Sarlin
Date: 2013-11-22 00:00:00
Tasks: Clustering

This paper proposes schemes for automated and weighted Self-Organizing Time
Maps (SOTMs). The SOTM provides means for a visual approach to evolutionary
clustering, which aims at producing a sequence of clustering solutions. This
task we denote as visual dynamic clustering. The implication of an automated
SOTM is not only a data-driven parametrization of the SOTM, but also the
feature of adjusting the training to the characteristics of the data at each
time step. The aim of the weighted SOTM is to improve learning from more
trustworthy or important data with an instance-varying weight. The schemes for
automated and weighted SOTMs are illustrated on two real-world datasets: (i)
country-level risk indicators to measure the evolution of global imbalances,
and (ii) credit applicant data to measure the evolution of firm-level credit
risks.
Learning Pairwise Graphical Models with Nonlinear Sufficient Statistics
Authors: Xiao-Tong Yuan, Ping Li, Tong Zhang
Date: 2013-11-21 00:00:00
Tasks: Computational Efficiency

We investigate a generic problem of learning pairwise exponential family
graphical models with pairwise sufficient statistics defined by a global
mapping function, e.g., Mercer kernels. This subclass of pairwise graphical
models allow us to flexibly capture complex interactions among variables beyond
pairwise product. We propose two $\ell_1$-norm penalized maximum likelihood
estimators to learn the model parameters from i.i.d. samples. The first one is
a joint estimator which estimates all the parameters simultaneously. The second
one is a node-wise conditional estimator which estimates the parameters
individually for each node. For both estimators, we show that under proper
conditions the extra flexibility gained in our model comes at almost no cost of
statistical and computational efficiency. We demonstrate the advantages of our
model over state-of-the-art methods on synthetic and real datasets.
Complexity measurement of natural and artificial languages
Authors: Gerardo Febres, Klaus Jaffe, Carlos Gershenson
Date: 2013-11-20 00:00:00
Tasks: Diversity

We compared entropy for texts written in natural languages (English, Spanish)
and artificial languages (computer software) based on a simple expression for
the entropy as a function of message length and specific word diversity. Code
text written in artificial languages showed higher entropy than text of similar
length expressed in natural languages. Spanish texts exhibit more symbolic
diversity than English ones. Results showed that algorithms based on complexity
measures differentiate artificial from natural languages, and that text
analysis based on complexity measures allows the unveiling of important aspects
of their nature. We propose specific expressions to examine entropy related
aspects of tests and estimate the values of entropy, emergence,
self-organization and complexity based on specific diversity and message
length.
Sparse Overlapping Sets Lasso for Multitask Learning and its Application to fMRI Analysis
Authors: Nikhil Rao, Christopher Cox, Robert Nowak, Timothy Rogers
Date: 2013-11-20 00:00:00
Proceeding: NeurIPS 2013 12

Multitask learning can be effective when features useful in one task are also
useful for other tasks, and the group lasso is a standard method for selecting
a common subset of features. In this paper, we are interested in a less
restrictive form of multitask learning, wherein (1) the available features can
be organized into subsets according to a notion of similarity and (2) features
useful in one task are similar, but not necessarily identical, to the features
best suited for other tasks. The main contribution of this paper is a new
procedure called Sparse Overlapping Sets (SOS) lasso, a convex optimization
that automatically selects similar features for related learning tasks. Error
bounds are derived for SOSlasso and its consistency is established for squared
error loss. In particular, SOSlasso is motivated by multi- subject fMRI studies
in which functional activity is classified using brain voxels as features.
Experiments with real and synthetic data demonstrate the advantages of SOSlasso
compared to the lasso and group lasso.
A quantum genetic algorithm with quantum crossover and mutation operations
Authors: Akira SaiToh, Robabeh Rahimi, Mikio Nakahara
Date: 2012-02-09 00:00:00

In the context of evolutionary quantum computing in the literal meaning, a
quantum crossover operation has not been introduced so far. Here, we introduce
a novel quantum genetic algorithm which has a quantum crossover procedure
performing crossovers among all chromosomes in parallel for each generation. A
complexity analysis shows that a quadratic speedup is achieved over its
classical counterpart in the dominant factor of the run time to handle each
generation.
Learning Non-Linear Feature Maps
Authors: Dimitrios Athanasakis, John Shawe-Taylor, Delmiro Fernandez-Reyes
Date: 2013-11-22 00:00:00
Tasks: feature selection

Feature selection plays a pivotal role in learning, particularly in areas
were parsimonious features can provide insight into the underlying process,
such as biology. Recent approaches for non-linear feature selection employing
greedy optimisation of Centred Kernel Target Alignment(KTA), while exhibiting
strong results in terms of generalisation accuracy and sparsity, can become
computationally prohibitive for high-dimensional datasets. We propose randSel,
a randomised feature selection algorithm, with attractive scaling properties.
Our theoretical analysis of randSel provides strong probabilistic guarantees
for the correct identification of relevant features. Experimental results on
real and artificial data, show that the method successfully identifies
effective features, performing better than a number of competitive approaches.
Compressive Measurement Designs for Estimating Structured Signals in Structured Clutter: A Bayesian Experimental Design Approach
Authors: Swayambhoo Jain, Akshay Soni, Jarvis Haupt
Date: 2013-11-21 00:00:00
Tasks: Compressive Sensing, Experimental Design

This work considers an estimation task in compressive sensing, where the goal
is to estimate an unknown signal from compressive measurements that are
corrupted by additive pre-measurement noise (interference, or clutter) as well
as post-measurement noise, in the specific setting where some (perhaps limited)
prior knowledge on the signal, interference, and noise is available. The
specific aim here is to devise a strategy for incorporating this prior
information into the design of an appropriate compressive measurement strategy.
Here, the prior information is interpreted as statistics of a prior
distribution on the relevant quantities, and an approach based on Bayesian
Experimental Design is proposed. Experimental results on synthetic data
demonstrate that the proposed approach outperforms traditional random
compressive measurement designs, which are agnostic to the prior information,
as well as several other knowledge-enhanced sensing matrix designs based on
more heuristic notions.