pwc-archive/papers-with-abstracts huggingface dataset:
Foreground segmentation based on multi-resolution and matting
Authors: Xintong Yu, Xiaohan Liu, Yisong Chen
Date: 2014-02-10 00:00:00
Tasks: Classification, Foreground Segmentation, General Classification, Image Matting, Segmentation

We propose a foreground segmentation algorithm that does foreground
extraction under different scales and refines the result by matting. First, the
input image is filtered and resampled to 5 different resolutions. Then each of
them is segmented by adaptive figure-ground classification and the best
segmentation is automatically selected by an evaluation score that maximizes
the difference between foreground and background. This segmentation is
upsampled to the original size, and a corresponding trimap is built.
Closed-form matting is employed to label the boundary region, and the result is
refined by a final figure-ground classification. Experiments show the success
of our method in treating challenging images with cluttered background and
adapting to loose initial bounding-box.
Recommandation mobile, sensible au contexte de contenus évolutifs: Contextuel-E-Greedy
Authors: Djallel Bouneffouf
Date: 2014-02-09 00:00:00

We introduce in this paper an algorithm named Contextuel-E-Greedy that
tackles the dynamicity of the user's content. It is based on dynamic
exploration/exploitation tradeoff and can adaptively balance the two aspects by
deciding which situation is most relevant for exploration or exploitation. The
experimental results demonstrate that our algorithm outperforms surveyed
algorithms.
Better Optimism By Bayes: Adaptive Planning with Rich Models
Authors: Arthur Guez, David Silver, Peter Dayan
Date: 2014-02-09 00:00:00
Tasks: Model-based Reinforcement Learning, Reinforcement Learning, Reinforcement Learning (RL), Thompson Sampling

The computational costs of inference and planning have confined Bayesian
model-based reinforcement learning to one of two dismal fates: powerful
Bayes-adaptive planning but only for simplistic models, or powerful, Bayesian
non-parametric models but using simple, myopic planning strategies such as
Thompson sampling. We ask whether it is feasible and truly beneficial to
combine rich probabilistic models with a closer approximation to fully Bayesian
planning. First, we use a collection of counterexamples to show formal problems
with the over-optimism inherent in Thompson sampling. Then we leverage
state-of-the-art techniques in efficient Bayes-adaptive planning and
non-parametric Bayesian methods to perform qualitatively better than both
existing conventional algorithms and Thompson sampling on two contextual
bandit-like problems.
Revisiting the Learned Clauses Database Reduction Strategies
Authors: Said Jabbour, Jerry Lonlac, Lakhdar Sais, Yakoub Salhi
Date: 2014-02-09 00:00:00
Tasks: Management

In this paper, we revisit an important issue of CDCL-based SAT solvers,
namely the learned clauses database management policies. Our motivation takes
its source from a simple observation on the remarkable performances of both
random and size-bounded reduction strategies. We first derive a simple
reduction strategy, called Size-Bounded Randomized strategy (in short SBR),
that combines maintaing short clauses (of size bounded by k), while deleting
randomly clauses of size greater than k. The resulting strategy outperform the
state-of-the-art, namely the LBD based one, on SAT instances taken from the
last SAT competition. Reinforced by the interest of keeping short clauses, we
propose several new dynamic variants, and we discuss their performances.
Classification Tree Diagrams in Health Informatics Applications
Authors: Farrukh Arslan
Date: 2014-02-09 00:00:00
Tasks: Anomaly Detection, Classification, General Classification, Retrieval

Health informatics deal with the methods used to optimize the acquisition,
storage and retrieval of medical data, and classify information in healthcare
applications. Healthcare analysts are particularly interested in various
computer informatics areas such as; knowledge representation from data, anomaly
detection, outbreak detection methods and syndromic surveillance applications.
Although various parametric and non-parametric approaches are being proposed to
classify information from data, classification tree diagrams provide an
interactive visualization to analysts as compared to other methods. In this
work we discuss application of classification tree diagrams to classify
information from medical data in healthcare applications.
MCA Learning Algorithm for Incident Signals Estimation: A Review
Authors: Rashid Ahmed, John A. Avaritsiotis
Date: 2014-02-09 00:00:00

Recently there has been many works on adaptive subspace filtering in the
signal processing literature. Most of them are concerned with tracking the
signal subspace spanned by the eigenvectors corresponding to the eigenvalues of
the covariance matrix of the signal plus noise data. Minor Component Analysis
(MCA) is important tool and has a wide application in telecommunications,
antenna array processing, statistical parametric estimation, etc. As an
important feature extraction technique, MCA is a statistical method of
extracting the eigenvector associated with the smallest eigenvalue of the
covariance matrix. In this paper, we will present a MCA learning algorithm to
extract minor component from input signals, and the learning rate parameter is
also presented, which ensures fast convergence of the algorithm, because it has
direct effect on the convergence of the weight vector and the error level is
affected by this value. MCA is performed to determine the estimated DOA.
Simulation results will be furnished to illustrate the theoretical results
achieved.
A Hybrid Loss for Multiclass and Structured Prediction
Authors: Qinfeng Shi, Mark Reid, Tiberio Caetano, Anton Van Den Hengel, Zhenhua Wang
Date: 2014-02-09 00:00:00
Tasks: Action Recognition, Prediction, Structured Prediction, Temporal Action Localization

We propose a novel hybrid loss for multiclass and structured prediction
problems that is a convex combination of a log loss for Conditional Random
Fields (CRFs) and a multiclass hinge loss for Support Vector Machines (SVMs).
We provide a sufficient condition for when the hybrid loss is Fisher consistent
for classification. This condition depends on a measure of dominance between
labels--specifically, the gap between the probabilities of the best label and
the second best label. We also prove Fisher consistency is necessary for
parametric consistency when learning models such as CRFs. We demonstrate
empirically that the hybrid loss typically performs least as well as--and often
better than--both of its constituent losses on a variety of tasks, such as
human action recognition. In doing so we also provide an empirical comparison
of the efficacy of probabilistic and margin based approaches to multiclass and
structured prediction.
StructBoost: Boosting Methods for Predicting Structured Output Variables
Authors: Chunhua Shen, Guosheng Lin, Anton Van Den Hengel
Date: 2013-02-14 00:00:00
Tasks: Image Segmentation, Multi-class Classification, Semantic Segmentation, Visual Tracking

Boosting is a method for learning a single accurate predictor by linearly combining a set of less accurate weak learners. Recently, structured learning has found many applications in computer vision. Inspired by structured support vector machines (SSVM), here we propose a new boosting algorithm for structured output prediction, which we refer to as StructBoost. StructBoost supports nonlinear structured learning by combining a set of weak structured learners. As SSVM generalizes SVM, our StructBoost generalizes standard boosting approaches such as AdaBoost, or LPBoost to structured learning. The resulting optimization problem of StructBoost is more challenging than SSVM in the sense that it may involve exponentially many variables and constraints. In contrast, for SSVM one usually has an exponential number of constraints and a cutting-plane method is used. In order to efficiently solve StructBoost, we formulate an equivalent $ 1 $-slack formulation and solve it using a combination of cutting planes and column generation. We show the versatility and usefulness of StructBoost on a range of problems such as optimizing the tree loss for hierarchical multi-class classification, optimizing the Pascal overlap criterion for robust visual tracking and learning conditional random field parameters for image segmentation.
Collaborative Discriminant Locality Preserving Projections With its Application to Face Recognition
Authors: Sheng Huang, Dan Yang, Dong Yang, Ahmed Elgammal
Date: 2013-12-28 00:00:00
Tasks: Face Recognition

We present a novel Discriminant Locality Preserving Projections (DLPP) algorithm named Collaborative Discriminant Locality Preserving Projection (CDLPP). In our algorithm, the discriminating power of DLPP are further exploited from two aspects. On the one hand, the global optimum of class scattering is guaranteed via using the between-class scatter matrix to replace the original denominator of DLPP. On the other hand, motivated by collaborative representation, an $L_2$-norm constraint is imposed to the projections to discover the collaborations of dimensions in the sample space. We apply our algorithm to face recognition. Three popular face databases, namely AR, ORL and LFW-A, are employed for evaluating the performance of CDLPP. Extensive experimental results demonstrate that CDLPP significantly improves the discriminating power of DLPP and outperforms the state-of-the-arts.
Sparse Illumination Learning and Transfer for Single-Sample Face Recognition with Image Corruption and Misalignment
Authors: Liansheng Zhuang, Tsung-Han Chan, Allen Y. Yang, S. Shankar Sastry, Yi Ma
Date: 2014-02-08 00:00:00
Tasks: Face Alignment, Face Recognition, Sparse Representation-based Classification

Single-sample face recognition is one of the most challenging problems in face recognition. We propose a novel algorithm to address this problem based on a sparse representation based classification (SRC) framework. The new algorithm is robust to image misalignment and pixel corruption, and is able to reduce required gallery images to one sample per class. To compensate for the missing illumination information traditionally provided by multiple gallery images, a sparse illumination learning and transfer (SILT) technique is introduced. The illumination in SILT is learned by fitting illumination examples of auxiliary face images from one or more additional subjects with a sparsely-used illumination dictionary. By enforcing a sparse representation of the query image in the illumination dictionary, the SILT can effectively recover and transfer the illumination and pose information from the alignment stage to the recognition stage. Our extensive experiments have demonstrated that the new algorithms significantly outperform the state of the art in the single-sample regime and with less restrictions. In particular, the single-sample face alignment accuracy is comparable to that of the well-known Deformable SRC algorithm using multiple gallery images per class. Furthermore, the face recognition accuracy exceeds those of the SRC and Extended SRC algorithms using hand labeled alignment initialization.
Excess Risk Bounds for Exponentially Concave Losses
Authors: Mehrdad Mahdavi, Rong Jin
Date: 2014-01-18 00:00:00
Tasks: Management

The overarching goal of this paper is to derive excess risk bounds for
learning from exp-concave loss functions in passive and sequential learning
settings. Exp-concave loss functions encompass several fundamental problems in
machine learning such as squared loss in linear regression, logistic loss in
classification, and negative logarithm loss in portfolio management. In batch
setting, we obtain sharp bounds on the performance of empirical risk
minimization performed in a linear hypothesis space and with respect to the
exp-concave loss functions. We also extend the results to the online setting
where the learner receives the training examples in a sequential manner. We
propose an online learning algorithm that is a properly modified version of
online Newton method to obtain sharp risk bounds. Under an additional mild
assumption on the loss function, we show that in both settings we are able to
achieve an excess risk bound of $O(d\log n/n)$ that holds with a high
probability.
Efficient Low Dose X-ray CT Reconstruction through Sparsity-Based MAP Modeling
Authors: SayedMasoud Hashemi, Soosan Beheshti, Patrick R. Gill, Narinder S. Paul, Richard S. C. Cobbold
Date: 2014-02-08 00:00:00
Tasks: compressed sensing, Compressive Sensing, Computed Tomography (CT), CT Reconstruction, Diagnostic, Low-Dose X-Ray Ct Reconstruction

Ultra low radiation dose in X-ray Computed Tomography (CT) is an important
clinical objective in order to minimize the risk of carcinogenesis. Compressed
Sensing (CS) enables significant reductions in radiation dose to be achieved by
producing diagnostic images from a limited number of CT projections. However,
the excessive computation time that conventional CS-based CT reconstruction
typically requires has limited clinical implementation. In this paper, we first
demonstrate that a thorough analysis of CT reconstruction through a Maximum a
Posteriori objective function results in a weighted compressive sensing
problem. This analysis enables us to formulate a low dose fan beam and helical
cone beam CT reconstruction. Subsequently, we provide an efficient solution to
the formulated CS problem based on a Fast Composite Splitting Algorithm-Latent
Expected Maximization (FCSA-LEM) algorithm. In the proposed method we use
pseudo polar Fourier transform as the measurement matrix in order to decrease
the computational complexity; and rebinning of the projections to parallel rays
in order to extend its application to fan beam and helical cone beam scans. The
weight involved in the proposed weighted CS model, denoted by Error Adaptation
Weight (EAW), is calculated based on the statistical characteristics of CT
reconstruction and is a function of Poisson measurement noise and rebinning
interpolation error. Simulation results show that low computational complexity
of the proposed method made the fast recovery of the CT images possible and
using EAW reduces the reconstruction error by one order of magnitude. Recovery
of a high quality 512$\times$ 512 image was achieved in less than 20 sec on a
desktop computer without numerical optimizations.
Binary Excess Risk for Smooth Convex Surrogates
Authors: Mehrdad Mahdavi, Lijun Zhang, Rong Jin
Date: 2014-02-07 00:00:00
Tasks: Learning Theory

In statistical learning theory, convex surrogates of the 0-1 loss are highly
preferred because of the computational and theoretical virtues that convexity
brings in. This is of more importance if we consider smooth surrogates as
witnessed by the fact that the smoothness is further beneficial both
computationally- by attaining an {\it optimal} convergence rate for
optimization, and in a statistical sense- by providing an improved {\it
optimistic} rate for generalization bound. In this paper we investigate the
smoothness property from the viewpoint of statistical consistency and show how
it affects the binary excess risk. We show that in contrast to optimization and
generalization errors that favor the choice of smooth surrogate loss, the
smoothness of loss function may degrade the binary excess risk. Motivated by
this negative result, we provide a unified analysis that integrates
optimization error, generalization bound, and the error in translating convex
excess risk into a binary excess risk when examining the impact of smoothness
on the binary excess risk. We show that under favorable conditions appropriate
choice of smooth convex loss will result in a binary excess risk that is better
than $O(1/\sqrt{n})$.
Frequency-Based Patrolling with Heterogeneous Agents and Limited Communication
Authors: Tao Mao, Laura Ray
Date: 2014-02-07 00:00:00
Tasks: Reinforcement Learning

This paper investigates multi-agent frequencybased patrolling of
intersecting, circle graphs under conditions where graph nodes have non-uniform
visitation requirements and agents have limited ability to communicate. The
task is modeled as a partially observable Markov decision process, and a
reinforcement learning solution is developed. Each agent generates its own
policy from Markov chains, and policies are exchanged only when agents occupy
the same or adjacent nodes. This constraint on policy exchange models sparse
communication conditions over large, unstructured environments. Empirical
results provide perspectives on convergence properties, agent cooperation, and
generalization of learned patrolling policies to new instances of the task. The
emergent behavior indicates learned coordination strategies between
heterogeneous agents for patrolling large, unstructured regions as well as the
ability to generalize to dynamic variation in node visitation requirements.
How Santa Fe Ants Evolve
Authors: Dominic Wilson, Devinder Kaur
Date: 2013-12-06 00:00:00

The Santa Fe Ant model problem has been extensively used to investigate, test
and evaluate Evolutionary Computing systems and methods over the past two
decades. There is however no literature on its program structures that are
systematically used for fitness improvement, the geometries of those structures
and their dynamics during optimization. This paper analyzes the Santa Fe Ant
Problem using a new phenotypic schema and landscape analysis based on executed
instruction sequences. For the first time we detail systematic structural
features that give high fitness and the evolutionary dynamics of such
structures. The new schema avoids variances due to introns. We develop a
phenotypic variation method that tests the new understanding of the landscape.
We also develop a modified function set that tests newly identified
synchronization constraints. We obtain favorable computational efforts compared
to those in the literature, on testing the new variation and function set on
both the Santa Fe Trail, and the more computationally demanding Los Altos
Trail. Our findings suggest that for the Santa Fe Ant problem, a perspective of
program assembly from repetition of highly fit responses to trail conditions
leads to better analysis and performance.
Alternating Minimization for Mixed Linear Regression
Authors: Xinyang Yi, Constantine Caramanis, Sujay Sanghavi
Date: 2013-10-14 00:00:00
Tasks: regression

Mixed linear regression involves the recovery of two (or more) unknown
vectors from unlabeled linear measurements; that is, where each sample comes
from exactly one of the vectors, but we do not know which one. It is a classic
problem, and the natural and empirically most popular approach to its solution
has been the EM algorithm. As in other settings, this is prone to bad local
minima; however, each iteration is very fast (alternating between guessing
labels, and solving with those labels).
  In this paper we provide a new initialization procedure for EM, based on
finding the leading two eigenvectors of an appropriate matrix. We then show
that with this, a re-sampled version of the EM algorithm provably converges to
the correct vectors, under natural assumptions on the sampling distribution,
and with nearly optimal (unimprovable) sample complexity. This provides not
only the first characterization of EM's performance, but also much lower sample
complexity as compared to both standard (randomly initialized) EM, and other
methods for this problem.
Performance of Hull-Detection Algorithms For Proton Computed Tomography Reconstruction
Authors: Blake Schultze, Micah Witt, Yair Censor, Reinhard Schulte, Keith Evan Schubert
Date: 2014-02-07 00:00:00
Tasks: Image Reconstruction

Proton computed tomography (pCT) is a novel imaging modality developed for
patients receiving proton radiation therapy. The purpose of this work was to
investigate hull-detection algorithms used for preconditioning of the large and
sparse linear system of equations that needs to be solved for pCT image
reconstruction. The hull-detection algorithms investigated here included
silhouette/space carving (SC), modified silhouette/space carving (MSC), and
space modeling (SM). Each was compared to the cone-beam version of filtered
backprojection (FBP) used for hull-detection. Data for testing these algorithms
included simulated data sets of a digital head phantom and an experimental data
set of a pediatric head phantom obtained with a pCT scanner prototype at Loma
Linda University Medical Center. SC was the fastest algorithm, exceeding the
speed of FBP by more than 100 times. FBP was most sensitive to the presence of
noise. Ongoing work will focus on optimizing threshold parameters in order to
define a fast and efficient method for hull-detection in pCT image
reconstruction.
A Statistical Learning Theory Framework for Supervised Pattern Discovery
Authors: Jonathan H. Huggins, Cynthia Rudin
Date: 2013-07-02 00:00:00
Tasks: Learning Theory

This paper formalizes a latent variable inference problem we call {\em
supervised pattern discovery}, the goal of which is to find sets of
observations that belong to a single ``pattern.'' We discuss two versions of
the problem and prove uniform risk bounds for both. In the first version,
collections of patterns can be generated in an arbitrary manner and the data
consist of multiple labeled collections. In the second version, the patterns
are assumed to be generated independently by identically distributed processes.
These processes are allowed to take an arbitrary form, so observations within a
pattern are not in general independent of each other. The bounds for the second
version of the problem are stated in terms of a new complexity measure, the
quasi-Rademacher complexity.
Evaluation of YTEX and MetaMap for clinical concept recognition
Authors: John David Osborne, Binod Gyawali, Thamar Solorio
Date: 2014-02-07 00:00:00

We used MetaMap and YTEX as a basis for the construc- tion of two separate
systems to participate in the 2013 ShARe/CLEF eHealth Task 1[9], the
recognition of clinical concepts. No modifications were directly made to these
systems, but output concepts were filtered using stop concepts, stop concept
text and UMLS semantic type. Con- cept boundaries were also adjusted using a
small collection of rules to increase precision on the strict task. Overall
MetaMap had better per- formance than YTEX on the strict task, primarily due to
a 20% perfor- mance improvement in precision. In the relaxed task YTEX had
better performance in both precision and recall giving it an overall F-Score
4.6% higher than MetaMap on the test data. Our results also indicated a 1.3%
higher accuracy for YTEX in UMLS CUI mapping.
A Brief History of Learning Classifier Systems: From CS-1 to XCS
Authors: Larry Bull
Date: 2014-01-15 00:00:00

Modern Learning Classifier Systems can be characterized by their use of rule
accuracy as the utility metric for the search algorithm(s) discovering useful
rules. Such searching typically takes place within the restricted space of
co-active rules for efficiency. This paper gives an historical overview of the
evolution of such systems up to XCS, and then some of the subsequent
developments of XCS to different types of learning.