pwc-archive/papers-with-abstracts huggingface dataset:
Robustness of Random Forest-based gene selection methods
Authors: Miron B. Kursa
Date: 2013-05-20 00:00:00
Tasks: feature selection

Gene selection is an important part of microarray data analysis because it
provides information that can lead to a better mechanistic understanding of an
investigated phenomenon. At the same time, gene selection is very difficult
because of the noisy nature of microarray data. As a consequence, gene
selection is often performed with machine learning methods. The Random Forest
method is particularly well suited for this purpose. In this work, four
state-of-the-art Random Forest-based feature selection methods were compared in
a gene selection context. The analysis focused on the stability of selection
because, although it is necessary for determining the significance of results,
it is often ignored in similar studies.
  The comparison of post-selection accuracy in the validation of Random Forest
classifiers revealed that all investigated methods were equivalent in this
context. However, the methods substantially differed with respect to the number
of selected genes and the stability of selection. Of the analysed methods, the
Boruta algorithm predicted the most genes as potentially important.
  The post-selection classifier error rate, which is a frequently used measure,
was found to be a potentially deceptive measure of gene selection quality. When
the number of consistently selected genes was considered, the Boruta algorithm
was clearly the best. Although it was also the most computationally intensive
method, the Boruta algorithm's computational demands could be reduced to levels
comparable to those of other algorithms by replacing the Random Forest
importance with a comparable measure from Random Ferns (a similar but
simplified classifier). Despite their design assumptions, the minimal optimal
selection methods, were found to select a high fraction of false positives.
Distributional semantics beyond words: Supervised learning of analogy and paraphrase
Authors: Peter D. Turney
Date: 2013-10-18 00:00:00
Proceeding: TACL 2013 1
Tasks: Multiple-choice, Task 2

There have been several efforts to extend distributional semantics beyond
individual words, to measure the similarity of word pairs, phrases, and
sentences (briefly, tuples; ordered sets of words, contiguous or
noncontiguous). One way to extend beyond words is to compare two tuples using a
function that combines pairwise similarities between the component words in the
tuples. A strength of this approach is that it works with both relational
similarity (analogy) and compositional similarity (paraphrase). However, past
work required hand-coding the combination function for different tasks. The
main contribution of this paper is that combination functions are generated by
supervised learning. We achieve state-of-the-art results in measuring
relational similarity between word pairs (SAT analogies and SemEval~2012 Task
2) and measuring compositional similarity between noun-modifier phrases and
unigrams (multiple-choice paraphrase questions).
Learning Tensors in Reproducing Kernel Hilbert Spaces with Multilinear Spectral Penalties
Authors: Marco Signoretto, Lieven De Lathauwer, Johan A. K. Suykens
Date: 2013-10-18 00:00:00
Tasks: Transfer Learning

We present a general framework to learn functions in tensor product
reproducing kernel Hilbert spaces (TP-RKHSs). The methodology is based on a
novel representer theorem suitable for existing as well as new spectral
penalties for tensors. When the functions in the TP-RKHS are defined on the
Cartesian product of finite discrete sets, in particular, our main problem
formulation admits as a special case existing tensor completion problems. Other
special cases include transfer learning with multimodal side information and
multilinear multitask learning. For the latter case, our kernel-based view is
instrumental to derive nonlinear extensions of existing model classes. We give
a novel algorithm and show in experiments the usefulness of the proposed
extensions.
A Logic-based Approach for Recognizing Textual Entailment Supported by Ontological Background Knowledge
Authors: Andreas Wotzlaw, Ravi Coote
Date: 2013-10-18 00:00:00
Tasks: Common Sense Reasoning, Natural Language Inference, RTE

We present the architecture and the evaluation of a new system for
recognizing textual entailment (RTE). In RTE we want to identify automatically
the type of a logical relation between two input texts. In particular, we are
interested in proving the existence of an entailment between them. We conceive
our system as a modular environment allowing for a high-coverage syntactic and
semantic text analysis combined with logical inference. For the syntactic and
semantic analysis we combine a deep semantic analysis with a shallow one
supported by statistical models in order to increase the quality and the
accuracy of results. For RTE we use logical inference of first-order employing
model-theoretic techniques and automated reasoning tools. The inference is
supported with problem-relevant background knowledge extracted automatically
and on demand from external sources like, e.g., WordNet, YAGO, and OpenCyc, or
other, more experimental sources with, e.g., manually defined presupposition
resolutions, or with axiomatized general and common sense knowledge. The
results show that fine-grained and consistent knowledge coming from diverse
sources is a necessary condition determining the correctness and traceability
of results.
On the Internal Topological Structure of Plane Regions
Authors: Sanjiang Li
Date: 2009-09-01 00:00:00
Tasks: Spatial Reasoning

The study of topological information of spatial objects has for a long time
been a focus of research in disciplines like computational geometry, spatial
reasoning, cognitive science, and robotics. While the majority of these
researches emphasised the topological relations between spatial objects, this
work studies the internal topological structure of bounded plane regions, which
could consist of multiple pieces and/or have holes and islands to any finite
level. The insufficiency of simple regions (regions homeomorphic to closed
disks) to cope with the variety and complexity of spatial entities and
phenomena has been widely acknowledged. Another significant drawback of simple
regions is that they are not closed under set operations union, intersection,
and difference. This paper considers bounded semi-algebraic regions, which are
closed under set operations and can closely approximate most plane regions
arising in practice.
Text Classification For Authorship Attribution Analysis
Authors: M. Sudheep Elayidom, Chinchu Jose, Anitta Puthussery, Neenu K Sasi
Date: 2013-10-18 00:00:00
Tasks: Authorship Attribution, Classification, General Classification, Sentence, text-classification, Text Classification

Authorship attribution mainly deals with undecided authorship of literary
texts. Authorship attribution is useful in resolving issues like uncertain
authorship, recognize authorship of unknown texts, spot plagiarism so on.
Statistical methods can be used to set apart the approach of an author
numerically. The basic methodologies that are made use in computational
stylometry are word length, sentence length, vocabulary affluence, frequencies
etc. Each author has an inborn style of writing, which is particular to
himself. Statistical quantitative techniques can be used to differentiate the
approach of an author in a numerical way. The problem can be broken down into
three sub problems as author identification, author characterization and
similarity detection. The steps involved are pre-processing, extracting
features, classification and author identification. For this different
classifiers can be used. Here fuzzy learning classifier and SVM are used. After
author identification the SVM was found to have more accuracy than Fuzzy
classifier. Later combined the classifiers to obtain a better accuracy when
compared to individual SVM and fuzzy classifier.
Dictionary Learning and Sparse Coding on Grassmann Manifolds: An Extrinsic Solution
Authors: Mehrtash Harandi, Conrad Sanderson, Chunhua Shen, Brian C. Lovell
Date: 2013-10-18 00:00:00
Tasks: Action Recognition, Dictionary Learning, Face Recognition, General Classification, Graph Embedding, Temporal Action Localization, Texture Classification

Recent advances in computer vision and machine learning suggest that a wide
range of problems can be addressed more appropriately by considering
non-Euclidean geometry. In this paper we explore sparse dictionary learning
over the space of linear subspaces, which form Riemannian structures known as
Grassmann manifolds. To this end, we propose to embed Grassmann manifolds into
the space of symmetric matrices by an isometric mapping, which enables us to
devise a closed-form solution for updating a Grassmann dictionary, atom by
atom. Furthermore, to handle non-linearity in data, we propose a kernelised
version of the dictionary learning algorithm. Experiments on several
classification tasks (face recognition, action recognition, dynamic texture
classification) show that the proposed approach achieves considerable
improvements in discrimination accuracy, in comparison to state-of-the-art
methods such as kernelised Affine Hull Method and graph-embedding Grassmann
discriminant analysis.
Analyzing Big Data with Dynamic Quantum Clustering
Authors: M. Weinstein, F. Meirer, A. Hume, Ph. Sciau, G. Shaked, R. Hofstetter, E. Persi, A. Mehta, D. Horn
Date: 2013-10-10 00:00:00
Tasks: Clustering

How does one search for a needle in a multi-dimensional haystack without
knowing what a needle is and without knowing if there is one in the haystack?
This kind of problem requires a paradigm shift - away from hypothesis driven
searches of the data - towards a methodology that lets the data speak for
itself. Dynamic Quantum Clustering (DQC) is such a methodology. DQC is a
powerful visual method that works with big, high-dimensional data. It exploits
variations of the density of the data (in feature space) and unearths subsets
of the data that exhibit correlations among all the measured variables. The
outcome of a DQC analysis is a movie that shows how and why sets of data-points
are eventually classified as members of simple clusters or as members of - what
we call - extended structures. This allows DQC to be successfully used in a
non-conventional exploratory mode where one searches data for unexpected
information without the need to model the data. We show how this works for big,
complex, real-world datasets that come from five distinct fields: i.e., x-ray
nano-chemistry, condensed matter, biology, seismology and finance. These
studies show how DQC excels at uncovering unexpected, small - but meaningful -
subsets of the data that contain important information. We also establish an
important new result: namely, that big, complex datasets often contain
interesting structures that will be missed by many conventional clustering
techniques. Experience shows that these structures appear frequently enough
that it is crucial to know they can exist, and that when they do, they encode
important hidden information. In short, we not only demonstrate that DQC can be
flexibly applied to datasets that present significantly different challenges,
we also show how a simple analysis can be used to look for the needle in the
haystack, determine what it is, and find what this means.
Fine-grained Categorization -- Short Summary of our Entry for the ImageNet Challenge 2012
Authors: Christoph Göring, Alexander Freytag, Erik Rodner, Joachim Denzler
Date: 2013-10-17 00:00:00

In this paper, we tackle the problem of visual categorization of dog breeds,
which is a surprisingly challenging task due to simultaneously present low
interclass distances and high intra-class variances. Our approach combines
several techniques well known in our community but often not utilized for
fine-grained recognition:
  (1) automatic segmentation, (2) efficient part detection, and (3) combination
of multiple features. In particular, we demonstrate that a simple head detector
embedded in an off-the-shelf recognition pipeline can improve recognition
accuracy quite significantly, highlighting the importance of part features for
fine-grained recognition tasks. Using our approach, we achieved a 24.59% mean
average precision performance on the Stanford dog dataset.
Effectiveness of pre- and inprocessing for CDCL-based SAT solving
Authors: Andreas Wotzlaw, Alexander van der Grinten, Ewald Speckenmeyer
Date: 2013-10-17 00:00:00

Applying pre- and inprocessing techniques to simplify CNF formulas both
before and during search can considerably improve the performance of modern SAT
solvers. These algorithms mostly aim at reducing the number of clauses,
literals, and variables in the formula. However, to be worthwhile, it is
necessary that their additional runtime does not exceed the runtime saved
during the subsequent SAT solver execution. In this paper we investigate the
efficiency and the practicability of selected simplification algorithms for
CDCL-based SAT solving. We first analyze them by means of their expected impact
on the CNF formula and SAT solving at all. While testing them on real-world and
combinatorial SAT instances, we show which techniques and combinations of them
yield a desirable speedup and which ones should be avoided.
Calibration of an Articulated Camera System with Scale Factor Estimation
Authors: Junzhou Chen, Kin Hong Wong
Date: 2013-10-17 00:00:00

Multiple Camera Systems (MCS) have been widely used in many vision
applications and attracted much attention recently. There are two principle
types of MCS, one is the Rigid Multiple Camera System (RMCS); the other is the
Articulated Camera System (ACS). In a RMCS, the relative poses (relative 3-D
position and orientation) between the cameras are invariant. While, in an ACS,
the cameras are articulated through movable joints, the relative pose between
them may change. Therefore, through calibration of an ACS we want to find not
only the relative poses between the cameras but also the positions of the
joints in the ACS.
  In this paper, we developed calibration algorithms for the ACS using a simple
constraint: the joint is fixed relative to the cameras connected with it during
the transformations of the ACS. When the transformations of the cameras in an
ACS can be estimated relative to the same coordinate system, the positions of
the joints in the ACS can be calculated by solving linear equations. However,
in a non-overlapping view ACS, only the ego-transformations of the cameras and
can be estimated. We proposed a two-steps method to deal with this problem. In
both methods, the ACS is assumed to have performed general transformations in a
static environment. The efficiency and robustness of the proposed methods are
tested by simulation and real experiments. In the real experiment, the
intrinsic and extrinsic parameters of the ACS are obtained simultaneously by
our calibration procedure using the same image sequences, no extra data
capturing step is required. The corresponding trajectory is recovered and
illustrated using the calibration results of the ACS. Since the estimated
translations of different cameras in an ACS may scaled by different scale
factors, a scale factor estimation algorithm is also proposed. To our
knowledge, we are the first to study the calibration of ACS.
The Partner Units Configuration Problem: Completing the Picture
Authors: Erich Christian Teppan, Gerhard Friedrich
Date: 2013-08-28 00:00:00
Tasks: Electrical Engineering, Heuristic Search

The partner units problem (PUP) is an acknowledged hard benchmark problem for
the Logic Programming community with various industrial application fields like
surveillance, electrical engineering, computer networks or railway safety
systems. However, computational complexity remained widely unclear so far. In
this paper we provide all missing complexity results making the PUP better
exploitable for benchmark testing. Furthermore, we present QuickPup, a
heuristic search algorithm for PUP instances which outperforms all
state-of-the-art solving approaches and which is already in use in real world
industrial configuration environments.
Discriminative Link Prediction using Local Links, Node Features and Community Structure
Authors: Abir De, Niloy Ganguly, Soumen Chakrabarti
Date: 2013-10-17 00:00:00
Tasks: Benchmarking, Clustering, Link Prediction

A link prediction (LP) algorithm is given a graph, and has to rank, for each
node, other nodes that are candidates for new linkage. LP is strongly motivated
by social search and recommendation applications. LP techniques often focus on
global properties (graph conductance, hitting or commute times, Katz score) or
local properties (Adamic-Adar and many variations, or node feature vectors),
but rarely combine these signals. Furthermore, neither of these extremes
exploit link densities at the intermediate level of communities. In this paper
we describe a discriminative LP algorithm that exploits two new signals. First,
a co-clustering algorithm provides community level link density estimates,
which are used to qualify observed links with a surprise value. Second, links
in the immediate neighborhood of the link to be predicted are not interpreted
at face value, but through a local model of node feature similarities. These
signals are combined into a discriminative link predictor. We evaluate the new
predictor using five diverse data sets that are standard in the literature. We
report on significant accuracy boosts compared to standard LP methods
(including Adamic-Adar and random walk). Apart from the new predictor, another
contribution is a rigorous protocol for benchmarking and reporting LP
algorithms, which reveals the regions of strengths and weaknesses of all the
predictors studied here, and establishes the new proposal as the most robust.
Thompson Sampling in Dynamic Systems for Contextual Bandit Problems
Authors: Tianbing Xu, Yaming Yu, John Turner, Amelia Regan
Date: 2013-10-17 00:00:00
Tasks: Thompson Sampling

We consider the multiarm bandit problems in the timevarying dynamic system
for rich structural features. For the nonlinear dynamic model, we propose the
approximate inference for the posterior distributions based on Laplace
Approximation. For the context bandit problems, Thompson Sampling is adopted
based on the underlying posterior distributions of the parameters. More
specifically, we introduce the discount decays on the previous samples impact
and analyze the different decay rates with the underlying sample dynamics.
Consequently, the exploration and exploitation is adaptively tradeoff according
to the dynamics in the system.
Online Classification Using a Voted RDA Method
Authors: Tianbing Xu, Jianfeng Gao, Lin Xiao, Amelia Regan
Date: 2013-10-17 00:00:00
Tasks: Classification, General Classification

We propose a voted dual averaging method for online classification problems
with explicit regularization. This method employs the update rule of the
regularized dual averaging (RDA) method, but only on the subsequence of
training examples where a classification error is made. We derive a bound on
the number of mistakes made by this method on the training set, as well as its
generalization error rate. We also introduce the concept of relative strength
of regularization, and show how it affects the mistake bound and generalization
performance. We experimented with the method using $\ell_1$ regularization on a
large-scale natural language processing task, and obtained state-of-the-art
classification performance with fairly sparse models.
A systematic comparison of supervised classifiers
Authors: D. R. Amancio, C. H. Comin, D. Casanova, G. Travieso, O. M. Bruno, F. A. Rodrigues, L. da F. Costa
Date: 2013-10-17 00:00:00
Tasks: General Classification

Pattern recognition techniques have been employed in a myriad of industrial,
medical, commercial and academic applications. To tackle such a diversity of
data, many techniques have been devised. However, despite the long tradition of
pattern recognition research, there is no technique that yields the best
classification in all scenarios. Therefore, the consideration of as many as
possible techniques presents itself as an fundamental practice in applications
aiming at high accuracy. Typical works comparing methods either emphasize the
performance of a given algorithm in validation tests or systematically compare
various algorithms, assuming that the practical use of these methods is done by
experts. In many occasions, however, researchers have to deal with their
practical classification tasks without an in-depth knowledge about the
underlying mechanisms behind parameters. Actually, the adequate choice of
classifiers and parameters alike in such practical circumstances constitutes a
long-standing problem and is the subject of the current paper. We carried out a
study on the performance of nine well-known classifiers implemented by the Weka
framework and compared the dependence of the accuracy with their configuration
parameter configurations. The analysis of performance with default parameters
revealed that the k-nearest neighbors method exceeds by a large margin the
other methods when high dimensional datasets are considered. When other
configuration of parameters were allowed, we found that it is possible to
improve the quality of SVM in more than 20% even if parameters are set
randomly. Taken together, the investigation conducted in this paper suggests
that, apart from the SVM implementation, Weka's default configuration of
parameters provides an performance close the one achieved with the optimal
configuration.
A New Monte Carlo Based Algorithm for the Gaussian Process Classification Problem
Authors: Amir F. Atiya, Hatem A. Fayed, Ahmed H. Abdel-Gawad
Date: 2013-02-28 00:00:00
Tasks: General Classification, regression

Gaussian process is a very promising novel technology that has been applied
to both the regression problem and the classification problem. While for the
regression problem it yields simple exact solutions, this is not the case for
the classification problem, because we encounter intractable integrals. In this
paper we develop a new derivation that transforms the problem into that of
evaluating the ratio of multivariate Gaussian orthant integrals. Moreover, we
develop a new Monte Carlo procedure that evaluates these integrals. It is based
on some aspects of bootstrap sampling and acceptancerejection. The proposed
approach has beneficial properties compared to the existing Markov Chain Monte
Carlo approach, such as simplicity, reliability, and speed.
Distributed Representations of Words and Phrases and their Compositionality
Authors: Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, Jeffrey Dean
Date: 2013-10-16 00:00:00
Proceeding: NeurIPS 2013 12

The recently introduced continuous Skip-gram model is an efficient method for
learning high-quality distributed vector representations that capture a large
number of precise syntactic and semantic word relationships. In this paper we
present several extensions that improve both the quality of the vectors and the
training speed. By subsampling of the frequent words we obtain significant
speedup and also learn more regular word representations. We also describe a
simple alternative to the hierarchical softmax called negative sampling. An
inherent limitation of word representations is their indifference to word order
and their inability to represent idiomatic phrases. For example, the meanings
of "Canada" and "Air" cannot be easily combined to obtain "Air Canada".
Motivated by this example, we present a simple method for finding phrases in
text, and show that learning good vector representations for millions of
phrases is possible.