pwc-archive/papers-with-abstracts huggingface dataset:
Sparse Dictionary-based Attributes for Action Recognition and Summarization
Authors: Qiang Qiu, Zhuolin Jiang, Rama Chellappa
Date: 2013-08-01 00:00:00
Tasks: Action Recognition, Dictionary Learning, Temporal Action Localization

We present an approach for dictionary learning of action attributes via
information maximization. We unify the class distribution and appearance
information into an objective function for learning a sparse dictionary of
action attributes. The objective function maximizes the mutual information
between what has been learned and what remains to be learned in terms of
appearance information and class distribution for each dictionary atom. We
propose a Gaussian Process (GP) model for sparse representation to optimize the
dictionary objective function. The sparse coding property allows a kernel with
compact support in GP to realize a very efficient dictionary learning process.
Hence we can describe an action video by a set of compact and discriminative
action attributes. More importantly, we can recognize modeled action categories
in a sparse feature space, which can be generalized to unseen and unmodeled
action categories. Experimental results demonstrate the effectiveness of our
approach in action recognition and summarization.
An efficient model-free estimation of multiclass conditional probability
Authors: Tu Xu, Junhui Wang
Date: 2012-09-22 00:00:00
Tasks: quantile regression, regression

Conventional multiclass conditional probability estimation methods, such as
Fisher's discriminate analysis and logistic regression, often require
restrictive distributional model assumption. In this paper, a model-free
estimation method is proposed to estimate multiclass conditional probability
through a series of conditional quantile regression functions. Specifically,
the conditional class probability is formulated as difference of corresponding
cumulative distribution functions, where the cumulative distribution functions
can be converted from the estimated conditional quantile regression functions.
The proposed estimation method is also efficient as its computation cost does
not increase exponentially with the number of classes. The theoretical and
numerical studies demonstrate that the proposed estimation method is highly
competitive against the existing competitors, especially when the number of
classes is relatively large.
Domain-invariant Face Recognition using Learned Low-rank Transformation
Authors: Qiang Qiu, Guillermo Sapiro, Ching-Hui Chen
Date: 2013-08-01 00:00:00
Tasks: Face Recognition, Object Recognition

We present a low-rank transformation approach to compensate for face variations due to changes in visual domains, such as pose and illumination. The key idea is to learn discriminative linear transformations for face images using matrix rank as the optimization criteria. The learned linear transformations restore a shared low-rank structure for faces from the same subject, and, at the same time, force a high-rank structure for faces from different subjects. In this way, among the transformed faces, we reduce variations caused by domain changes within the classes, and increase separations between the classes for better face recognition across domains. Extensive experiments using public datasets are presented to demonstrate the effectiveness of our approach for face recognition across domains. The potential of the approach for feature extraction in generic object recognition and coded aperture design are discussed as well.
Learning Robust Subspace Clustering
Authors: Qiang Qiu, Guillermo Sapiro
Date: 2013-08-01 00:00:00
Tasks: Clustering

We propose a low-rank transformation-learning framework to robustify subspace
clustering. Many high-dimensional data, such as face images and motion
sequences, lie in a union of low-dimensional subspaces. The subspace clustering
problem has been extensively studied in the literature to partition such
high-dimensional data into clusters corresponding to their underlying
low-dimensional subspaces. However, low-dimensional intrinsic structures are
often violated for real-world observations, as they can be corrupted by errors
or deviate from ideal models. We propose to address this by learning a linear
transformation on subspaces using matrix rank, via its convex surrogate nuclear
norm, as the optimization criteria. The learned linear transformation restores
a low-rank structure for data from the same subspace, and, at the same time,
forces a high-rank structure for data from different subspaces. In this way, we
reduce variations within the subspaces, and increase separations between the
subspaces for more accurate subspace clustering. This proposed learned robust
subspace clustering framework significantly enhances the performance of
existing subspace clustering methods. To exploit the low-rank structures of the
transformed subspaces, we further introduce a subspace clustering technique,
called Robust Sparse Subspace Clustering, which efficiently combines robust PCA
with sparse modeling. We also discuss the online learning of the
transformation, and learning of the transformation while simultaneously
reducing the data dimensionality. Extensive experiments using public datasets
are presented, showing that the proposed approach significantly outperforms
state-of-the-art subspace clustering methods.
Knowledge Representation for Robots through Human-Robot Interaction
Authors: Emanuele Bastianelli, Domenico Bloisi, Roberto Capobianco, Guglielmo Gemignani, Luca Iocchi, Daniele Nardi
Date: 2013-07-28 00:00:00

The representation of the knowledge needed by a robot to perform complex
tasks is restricted by the limitations of perception. One possible way of
overcoming this situation and designing "knowledgeable" robots is to rely on
the interaction with the user. We propose a multi-modal interaction framework
that allows to effectively acquire knowledge about the environment where the
robot operates. In particular, in this paper we present a rich representation
framework that can be automatically built from the metric map annotated with
the indications provided by the user. Such a representation, allows then the
robot to ground complex referential expressions for motion commands and to
devise topological navigation plans to achieve the target locations.
The blessing of transitivity in sparse and stochastic networks
Authors: Karl Rohe, Tai Qin
Date: 2013-07-08 00:00:00
Tasks: Clustering

The interaction between transitivity and sparsity, two common features in
empirical networks, implies that there are local regions of large sparse
networks that are dense. We call this the blessing of transitivity and it has
consequences for both modeling and inference. Extant research suggests that
statistical inference for the Stochastic Blockmodel is more difficult when the
edges are sparse. However, this conclusion is confounded by the fact that the
asymptotic limit in all of the previous studies is not merely sparse, but also
non-transitive. To retain transitivity, the blocks cannot grow faster than the
expected degree. Thus, in sparse models, the blocks must remain asymptotically
small. \n Previous algorithmic research demonstrates that small "local"
clusters are more amenable to computation, visualization, and interpretation
when compared to "global" graph partitions. This paper provides the first
statistical results that demonstrate how these small transitive clusters are
also more amenable to statistical estimation. Theorem 2 shows that a "local"
clustering algorithm can, with high probability, detect a transitive stochastic
block of a fixed size (e.g. 30 nodes) embedded in a large graph. The only
constraint on the ambient graph is that it is large and sparse--it could be
generated at random or by an adversary--suggesting a theoretical explanation
for the robust empirical performance of local clustering algorithms.
An n-ary Constraint for the Stable Marriage Problem
Authors: Chris Unsworth, Patrick Prosser
Date: 2013-08-01 00:00:00
Tasks: ARC

We present an n-ary constraint for the stable marriage problem. This
constraint acts between two sets of integer variables where the domains of
those variables represent preferences. Our constraint enforces stability and
disallows bigamy. For a stable marriage instance with $n$ men and $n$ women we
require only one of these constraints, and the complexity of enforcing
arc-consistency is $O(n^2)$ which is optimal in the size of input. Our
computational studies show that our n-ary constraint is significantly faster
and more space efficient than the encodings presented in \cite{cp01}. We also
introduce a new problem to the constraint community, the sex-equal stable
marriage problem.
A better Beta for the H measure of classification performance
Authors: David J. Hand, Christoforos Anagnostopoulos
Date: 2012-02-12 00:00:00
Tasks: Classification, General Classification

The area under the ROC curve is widely used as a measure of performance of
classification rules. However, it has recently been shown that the measure is
fundamentally incoherent, in the sense that it treats the relative severities
of misclassifications differently when different classifiers are used. To
overcome this, Hand (2009) proposed the $H$ measure, which allows a given
researcher to fix the distribution of relative severities to a
classifier-independent setting on a given problem. This note extends the
discussion, and proposes a modified standard distribution for the $H$ measure,
which better matches the requirements of researchers, in particular those faced
with heavily unbalanced datasets, the $Beta(\pi_1+1,\pi_0+1)$ distribution.
[Preprint submitted at Pattern Recognition Letters]
Decision-Theoretic Troubleshooting: Hardness of Approximation
Authors: Václav Lín
Date: 2013-04-24 00:00:00

Decision-theoretic troubleshooting is one of the areas to which Bayesian
networks can be applied. Given a probabilistic model of a malfunctioning
man-made device, the task is to construct a repair strategy with minimal
expected cost. The problem has received considerable attention over the past
two decades. Efficient solution algorithms have been found for simple cases,
whereas other variants have been proven NP-complete. We study several variants
of the problem found in literature, and prove that computing approximate
troubleshooting strategies is NP-hard. In the proofs, we exploit a close
connection to set-covering problems.
Scoring and Searching over Bayesian Networks with Causal and Associative Priors
Authors: Giorgos Borboudakis, Ioannis Tsamardinos
Date: 2012-09-28 00:00:00

A significant theoretical advantage of search-and-score methods for learning
Bayesian Networks is that they can accept informative prior beliefs for each
possible network, thus complementing the data. In this paper, a method is
presented for assigning priors based on beliefs on the presence or absence of
certain paths in the true network. Such beliefs correspond to knowledge about
the possible causal and associative relations between pairs of variables. This
type of knowledge naturally arises from prior experimental and observational
data, among others. In addition, a novel search-operator is proposed to take
advantage of such prior knowledge. Experiments show that, using path beliefs
improves the learning of the skeleton, as well as the edge directions in the
network.
Fast Simultaneous Training of Generalized Linear Models (FaSTGLZ)
Authors: Bryan R. Conroy, Jennifer M. Walz, Brian Cheung, Paul Sajda
Date: 2013-07-31 00:00:00
Tasks: General Classification

We present an efficient algorithm for simultaneously training sparse
generalized linear models across many related problems, which may arise from
bootstrapping, cross-validation and nonparametric permutation testing. Our
approach leverages the redundancies across problems to obtain significant
computational improvements relative to solving the problems sequentially by a
conventional algorithm. We demonstrate our fast simultaneous training of
generalized linear models (FaSTGLZ) algorithm on a number of real-world
datasets, and we run otherwise computationally intensive bootstrapping and
permutation test analyses that are typically necessary for obtaining
statistically rigorous classification results and meaningful interpretation.
Code is freely available at http://liinc.bme.columbia.edu/fastglz.
Who and Where: People and Location Co-Clustering
Authors: Zixuan Wang, Jinyun Yan
Date: 2013-07-31 00:00:00
Tasks: Clustering

In this paper, we consider the clustering problem on images where each image
contains patches in people and location domains. We exploit the correlation
between people and location domains, and proposed a semi-supervised
co-clustering algorithm to cluster images. Our algorithm updates the
correlation links at the runtime, and produces clustering in both domains
simultaneously. We conduct experiments in a manually collected dataset and a
Flickr dataset. The result shows that the such correlation improves the
clustering performance.
The Planning-ahead SMO Algorithm
Authors: Tobias Glasmachers
Date: 2013-07-31 00:00:00

The sequential minimal optimization (SMO) algorithm and variants thereof are
the de facto standard method for solving large quadratic programs for support
vector machine (SVM) training. In this paper we propose a simple yet powerful
modification. The main emphasis is on an algorithm improving the SMO step size
by planning-ahead. The theoretical analysis ensures its convergence to the
optimum. Experiments involving a large number of datasets were carried out to
demonstrate the superiority of the new algorithm.
Tracking Extrema in Dynamic Environment using Multi-Swarm Cellular PSO with Local Search
Authors: Somayeh Nabizadeh, Alireza Rezvanian, Mohammad Reza Meybodi
Date: 2013-07-31 00:00:00
Tasks: Clustering

Many real-world phenomena can be modelled as dynamic optimization problems.
In such cases, the environment problem changes dynamically and therefore,
conventional methods are not capable of dealing with such problems. In this
paper, a novel multi-swarm cellular particle swarm optimization algorithm is
proposed by clustering and local search. In the proposed algorithm, the search
space is partitioned into cells, while the particles identify changes in the
search space and form clusters to create sub-swarms. Then a local search is
applied to improve the solutions in the each cell. Simulation results for
static standard benchmarks and dynamic environments show superiority of the
proposed method over other alternative approaches.
A Prototyping Environment for Integrated Artificial Attention Systems
Authors: Jan Tünnermann, Markus Hennig, Michael Silbernagel, Bärbel Mertsching
Date: 2013-07-31 00:00:00

Artificial visual attention systems aim to support technical systems in
visual tasks by applying the concepts of selective attention observed in humans
and other animals. Such systems are typically evaluated against ground truth
obtained from human gaze-data or manually annotated test images. When applied
to robotics, the systems are required to be adaptable to the target system.
Here, we describe a flexible environment based on a robotic middleware layer
allowing the development and testing of attention-guided vision systems. In
such a framework, the systems can be tested with input from various sources,
different attention algorithms at the core, and diverse subsequent tasks.
A Novel Architecture for Relevant Blog Page Identifcation
Authors: Deepti Kapri, Rosy Madaan, A. K. Sharma, Ashutosh Dixit
Date: 2013-07-31 00:00:00

Blogs are undoubtedly the richest source of information available in
cyberspace. Blogs can be of various natures i.e. personal blogs which contain
posts on mixed issues or blogs can be domain specific which contains posts on
particular topics, this is the reason, they offer wide variety of relevant
information which is often focused. A general search engine gives back a huge
collection of web pages which may or may not give correct answers, as web is
the repository of information of all kinds and a user has to go through various
documents before he gets what he was originally looking for, which is a very
time consuming process. So, the search can be made more focused and accurate if
it is limited to blogosphere instead of web pages. The reason being that the
blogs are more focused in terms of information. So, User will only get related
blogs in response to his query. These results will be then ranked according to
our proposed method and are finally presented in front of user in descending
order
POMDPs Make Better Hackers: Accounting for Uncertainty in Penetration Testing
Authors: Carlos Sarraute, Olivier Buffet, Joerg Hoffmann
Date: 2013-07-31 00:00:00

Penetration Testing is a methodology for assessing network security, by
generating and executing possible hacking attacks. Doing so automatically
allows for regular and systematic testing. A key question is how to generate
the attacks. This is naturally formulated as planning under uncertainty, i.e.,
under incomplete knowledge about the network configuration. Previous work uses
classical planning, and requires costly pre-processes reducing this uncertainty
by extensive application of scanning methods. By contrast, we herein model the
attack planning problem in terms of partially observable Markov decision
processes (POMDP). This allows to reason about the knowledge available, and to
intelligently employ scanning actions as part of the attack. As one would
expect, this accurate solution does not scale. We devise a method that relies
on POMDPs to find good attacks on individual machines, which are then composed
into an attack on the network as a whole. This decomposition exploits network
structure to the extent possible, making targeted approximations (only) where
needed. Evaluating this method on a suitably adapted industrial test suite, we
demonstrate its effectiveness in both runtime and solution quality.
DeBaCl: A Python Package for Interactive DEnsity-BAsed CLustering
Authors: Brian P. Kent, Alessandro Rinaldo, Timothy Verstynen
Date: 2013-07-30 00:00:00
Tasks: Clustering, Computational Efficiency

The level set tree approach of Hartigan (1975) provides a probabilistically
based and highly interpretable encoding of the clustering behavior of a
dataset. By representing the hierarchy of data modes as a dendrogram of the
level sets of a density estimator, this approach offers many advantages for
exploratory analysis and clustering, especially for complex and
high-dimensional data. Several R packages exist for level set tree estimation,
but their practical usefulness is limited by computational inefficiency,
absence of interactive graphical capabilities and, from a theoretical
perspective, reliance on asymptotic approximations. To make it easier for
practitioners to capture the advantages of level set trees, we have written the
Python package DeBaCl for DEnsity-BAsed CLustering. In this article we
illustrate how DeBaCl's level set tree estimates can be used for difficult
clustering tasks and interactive graphical data analysis. The package is
intended to promote the practical use of level set trees through improvements
in computational efficiency and a high degree of user customization. In
addition, the flexible algorithms implemented in DeBaCl enjoy finite sample
accuracy, as demonstrated in recent literature on density clustering. Finally,
we show the level set tree framework can be easily extended to deal with
functional data.
Neural Network Capacity for Multilevel Inputs
Authors: Matt Stowe, Subhash Kak
Date: 2013-07-30 00:00:00

This paper examines the memory capacity of generalized neural networks.
Hopfield networks trained with a variety of learning techniques are
investigated for their capacity both for binary and non-binary alphabets. It is
shown that the capacity can be much increased when multilevel inputs are used.
New learning strategies are proposed to increase Hopfield network capacity, and
the scalability of these methods is also examined in respect to size of the
network. The ability to recall entire patterns from stimulation of a single
neuron is examined for the increased capacity networks.
Extracting Information-rich Part of Texts using Text Denoising
Authors: Rushdi Shams
Date: 2013-07-30 00:00:00
Tasks: Denoising

The aim of this paper is to report on a novel text reduction technique,
called Text Denoising, that highlights information-rich content when processing
a large volume of text data, especially from the biomedical domain. The core
feature of the technique, the text readability index, embodies the hypothesis
that complex text is more information-rich than the rest. When applied on tasks
like biomedical relation bearing text extraction, keyphrase indexing and
extracting sentences describing protein interactions, it is evident that the
reduced set of text produced by text denoising is more information-rich than
the rest.
Extracting Connected Concepts from Biomedical Texts using Fog Index
Authors: Rushdi Shams, Robert E. Mercer
Date: 2013-07-30 00:00:00

In this paper, we establish Fog Index (FI) as a text filter to locate the
sentences in texts that contain connected biomedical concepts of interest. To
do so, we have used 24 random papers each containing four pairs of connected
concepts. For each pair, we categorize sentences based on whether they contain
both, any or none of the concepts. We then use FI to measure difficulty of the
sentences of each category and find that sentences containing both of the
concepts have low readability. We rank sentences of a text according to their
FI and select 30 percent of the most difficult sentences. We use an association
matrix to track the most frequent pairs of concepts in them. This matrix
reports that the first filter produces some pairs that hold almost no
connections. To remove these unwanted pairs, we use the Equally Weighted
Harmonic Mean of their Positive Predictive Value (PPV) and Sensitivity as a
second filter. Experimental results demonstrate the effectiveness of our
method.
Optimistic Concurrency Control for Distributed Unsupervised Learning
Authors: Xinghao Pan, Joseph E. Gonzalez, Stefanie Jegelka, Tamara Broderick, Michael. I. Jordan
Date: 2013-07-30 00:00:00
Proceeding: NeurIPS 2013 12
Tasks: BIG-bench Machine Learning, Clustering

Research on distributed machine learning algorithms has focused primarily on
one of two extremes - algorithms that obey strict concurrency constraints or
algorithms that obey few or no such constraints. We consider an intermediate
alternative in which algorithms optimistically assume that conflicts are
unlikely and if conflicts do arise a conflict-resolution protocol is invoked.
We view this "optimistic concurrency control" paradigm as particularly
appropriate for large-scale machine learning algorithms, particularly in the
unsupervised setting. We demonstrate our approach in three problem areas:
clustering, feature learning and online facility location. We evaluate our
methods via large-scale experiments in a cluster computing environment.