pwc-archive/papers-with-abstracts huggingface dataset:
Inference, Sampling, and Learning in Copula Cumulative Distribution Networks
Authors: Stefan Douglas Webb
Date: 2013-10-16 00:00:00

The cumulative distribution network (CDN) is a recently developed class of
probabilistic graphical models (PGMs) permitting a copula factorization, in
which the CDF, rather than the density, is factored. Despite there being much
recent interest within the machine learning community about copula
representations, there has been scarce research into the CDN, its amalgamation
with copula theory, and no evaluation of its performance. Algorithms for
inference, sampling, and learning in these models are underdeveloped compared
those of other PGMs, hindering widerspread use.
  One advantage of the CDN is that it allows the factors to be parameterized as
copulae, combining the benefits of graphical models with those of copula
theory. In brief, the use of a copula parameterization enables greater
modelling flexibility by separating representation of the marginals from the
dependence structure, permitting more efficient and robust learning. Another
advantage is that the CDN permits the representation of implicit latent
variables, whose parameterization and connectivity are not required to be
specified. Unfortunately, that the model can encode only latent relationships
between variables severely limits its utility.
  In this thesis, we present inference, learning, and sampling for CDNs, and
further the state-of-the-art. First, we explain the basics of copula theory and
the representation of copula CDNs. Then, we discuss inference in the models,
and develop the first sampling algorithm. We explain standard learning methods,
propose an algorithm for learning from data missing completely at random
(MCAR), and develop a novel algorithm for learning models of arbitrary
treewidth and size. Properties of the models and algorithms are investigated
through Monte Carlo simulations. We conclude with further discussion of the
advantages and limitations of CDNs, and suggest future work.
Multiple Attractor Cellular Automata (MACA) for Addressing Major Problems in Bioinformatics
Authors: Pokkuluri Kiran Sree, Inampudi Ramesh Babu, SSSN Usha Devi Nedunuri
Date: 2013-10-16 00:00:00

CA has grown as potential classifier for addressing major problems in
bioinformatics. Lot of bioinformatics problems like predicting the protein
coding region, finding the promoter region, predicting the structure of protein
and many other problems in bioinformatics can be addressed through Cellular
Automata. Even though there are some prediction techniques addressing these
problems, the approximate accuracy level is very less. An automated procedure
was proposed with MACA (Multiple Attractor Cellular Automata) which can address
all these problems. The genetic algorithm is also used to find rules with good
fitness values. Extensive experiments are conducted for reporting the accuracy
of the proposed tool. The average accuracy of MACA when tested with ENCODE,
BG570, HMR195, Fickett and Tongue, ASP67 datasets is 78%.
An FCA-based Boolean Matrix Factorisation for Collaborative Filtering
Authors: Elena Nenova, Dmitry I. Ignatov, Andrey V. Konstantinov
Date: 2013-10-16 00:00:00
Tasks: Collaborative Filtering

We propose a new approach for Collaborative Filtering which is based on
Boolean Matrix Factorisation (BMF) and Formal Concept Analysis. In a series of
experiments on real data (Movielens dataset) we compare the approach with the
SVD- and NMF-based algorithms in terms of Mean Average Error (MAE). One of the
experimental consequences is that it is enough to have a binary-scaled rating
data to obtain almost the same quality in terms of MAE by BMF than for the
SVD-based algorithm in case of non-scaled data.
Bayesian Information Sharing Between Noise And Regression Models Improves Prediction of Weak Effects
Authors: Jussi Gillberg, Pekka Marttinen, Matti Pirinen, Antti J. Kangas, Pasi Soininen, Marjo-Riitta Järvelin, Mika Ala-Korpela, Samuel Kaski
Date: 2013-10-16 00:00:00
Tasks: Prediction, regression

We consider the prediction of weak effects in a multiple-output regression
setup, when covariates are expected to explain a small amount, less than
$\approx 1%$, of the variance of the target variables. To facilitate the
prediction of the weak effects, we constrain our model structure by introducing
a novel Bayesian approach of sharing information between the regression model
and the noise model. Further reduction of the effective number of parameters is
achieved by introducing an infinite shrinkage prior and group sparsity in the
context of the Bayesian reduced rank regression, and using the Bayesian
infinite factor model as a flexible low-rank noise model. In our experiments
the model incorporating the novelties outperformed alternatives in genomic
prediction of rich phenotype data. In particular, the information sharing
between the noise and regression models led to significant improvement in
prediction accuracy.
An Extensive Report on Cellular Automata Based Artificial Immune System for Strengthening Automated Protein Prediction
Authors: Pokkuluri Kiran Sree, Inampudi Ramesh Babuhor, SSSN Usha Devi N3
Date: 2013-10-16 00:00:00
Tasks: feature selection

Artificial Immune System (AIS-MACA) a novel computational intelligence
technique is can be used for strengthening the automated protein prediction
system with more adaptability and incorporating more parallelism to the system.
Most of the existing approaches are sequential which will classify the input
into four major classes and these are designed for similar sequences. AIS-MACA
is designed to identify ten classes from the sequences that share twilight zone
similarity and identity with the training sequences with mixed and hybrid
variations. This method also predicts three states (helix, strand, and coil)
for the secondary structure. Our comprehensive design considers 10 feature
selection methods and 4 classifiers to develop MACA (Multiple Attractor
Cellular Automata) based classifiers that are build for each of the ten
classes. We have tested the proposed classifier with twilight-zone and
1-high-similarity benchmark datasets with over three dozens of modern competing
predictors shows that AIS-MACA provides the best overall accuracy that ranges
between 80% and 89.8% depending on the dataset.
SAT-based Preprocessing for MaxSAT (extended version)
Authors: Anton Belov, Antonio Morgado, Joao Marques-Silva
Date: 2013-10-08 00:00:00

State-of-the-art algorithms for industrial instances of MaxSAT problem rely
on iterative calls to a SAT solver. Preprocessing is crucial for the
acceleration of SAT solving, and the key preprocessing techniques rely on the
application of resolution and subsumption elimination. Additionally,
satisfiability-preserving clause elimination procedures are often used. Since
MaxSAT computation typically involves a large number of SAT calls, we are
interested in whether an input instance to a MaxSAT problem can be preprocessed
up-front, i.e. prior to running the MaxSAT solver, rather than (or, in addition
to) during each iterative SAT solver call. The key requirement in this setting
is that the preprocessing has to be sound, i.e. so that the solution can be
reconstructed correctly and efficiently after the execution of a MaxSAT
algorithm on the preprocessed instance. While, as we demonstrate in this paper,
certain clause elimination procedures are sound for MaxSAT, it is well-known
that this is not the case for resolution and subsumption elimination. In this
paper we show how to adapt these preprocessing techniques to MaxSAT. To achieve
this we recast the MaxSAT problem in a recently introduced labelled-CNF
framework, and show that within the framework the preprocessing techniques can
be applied soundly. Furthermore, we show that MaxSAT algorithms restated in the
framework have a natural implementation on top of an incremental SAT solver. We
evaluate the prototype implementation of a MaxSAT algorithm WMSU1 in this
setting, demonstrate the effectiveness of preprocessing, and show overall
improvement with respect to non-incremental versions of the algorithm on some
classes of problems.
Supervised Heterogeneous Multiview Learning for Joint Association Study and Disease Diagnosis
Authors: Shandian Zhe, Zenglin Xu, Yuan Qi
Date: 2013-04-26 00:00:00
Tasks: Multiview Learning

Given genetic variations and various phenotypical traits, such as Magnetic
Resonance Imaging (MRI) features, we consider two important and related tasks
in biomedical research: i)to select genetic and phenotypical markers for
disease diagnosis and ii) to identify associations between genetic and
phenotypical data. These two tasks are tightly coupled because underlying
associations between genetic variations and phenotypical features contain the
biological basis for a disease. While a variety of sparse models have been
applied for disease diagnosis and canonical correlation analysis and its
extensions have bee widely used in association studies (e.g., eQTL analysis),
these two tasks have been treated separately. To unify these two tasks, we
present a new sparse Bayesian approach for joint association study and disease
diagnosis. In this approach, common latent features are extracted from
different data sources based on sparse projection matrices and used to predict
multiple disease severity levels based on Gaussian process ordinal regression;
in return, the disease status is used to guide the discovery of relationships
between the data sources. The sparse projection matrices not only reveal
interactions between data sources but also select groups of biomarkers related
to the disease. To learn the model from data, we develop an efficient
variational expectation maximization algorithm. Simulation results demonstrate
that our approach achieves higher accuracy in both predicting ordinal labels
and discovering associations between data sources than alternative methods. We
apply our approach to an imaging genetics dataset for the study of Alzheimer's
Disease (AD). Our method identifies biologically meaningful relationships
between genetic variations, MRI features, and AD status, and achieves
significantly higher accuracy for predicting ordinal AD stages than the
competing methods.
Multilabel Consensus Classification
Authors: Sihong Xie, Xiangnan Kong, Jing Gao, Wei Fan, Philip S. Yu
Date: 2013-10-16 00:00:00
Tasks: Classification, General Classification, Prediction

In the era of big data, a large amount of noisy and incomplete data can be
collected from multiple sources for prediction tasks. Combining multiple models
or data sources helps to counteract the effects of low data quality and the
bias of any single model or data source, and thus can improve the robustness
and the performance of predictive models. Out of privacy, storage and bandwidth
considerations, in certain circumstances one has to combine the predictions
from multiple models or data sources to obtain the final predictions without
accessing the raw data. Consensus-based prediction combination algorithms are
effective for such situations. However, current research on prediction
combination focuses on the single label setting, where an instance can have one
and only one label. Nonetheless, data nowadays are usually multilabeled, such
that more than one label have to be predicted at the same time. Direct
applications of existing prediction combination methods to multilabel settings
can lead to degenerated performance. In this paper, we address the challenges
of combining predictions from multiple multilabel classifiers and propose two
novel algorithms, MLCM-r (MultiLabel Consensus Maximization for ranking) and
MLCM-a (MLCM for microAUC). These algorithms can capture label correlations
that are common in multilabel classifications, and optimize corresponding
performance metrics. Experimental results on popular multilabel classification
tasks verify the theoretical analysis and effectiveness of the proposed
methods.
On the Fundamental Limits of Recovering Tree Sparse Vectors from Noisy Linear Measurements
Authors: Akshay Soni, Jarvis Haupt
Date: 2013-06-18 00:00:00
Tasks: Compressive Sensing

Recent breakthrough results in compressive sensing (CS) have established that
many high dimensional signals can be accurately recovered from a relatively
small number of non-adaptive linear observations, provided that the signals
possess a sparse representation in some basis. Subsequent efforts have shown
that the performance of CS can be improved by exploiting additional structure
in the locations of the nonzero signal coefficients during inference, or by
utilizing some form of data-dependent adaptive measurement focusing during the
sensing process. To our knowledge, our own previous work was the first to
establish the potential benefits that can be achieved when fusing the notions
of adaptive sensing and structured sparsity -- that work examined the task of
support recovery from noisy linear measurements, and established that an
adaptive sensing strategy specifically tailored to signals that are tree-sparse
can significantly outperform adaptive and non-adaptive sensing strategies that
are agnostic to the underlying structure. In this work we establish fundamental
performance limits for the task of support recovery of tree-sparse signals from
noisy measurements, in settings where measurements may be obtained either
non-adaptively (using a randomized Gaussian measurement strategy motivated by
initial CS investigations) or by any adaptive sensing strategy. Our main
results here imply that the adaptive tree sensing procedure analyzed in our
previous work is nearly optimal, in the sense that no other sensing and
estimation strategy can perform fundamentally better for identifying the
support of tree-sparse signals.
Distributed Low-rank Subspace Segmentation
Authors: Ameet Talwalkar, Lester Mackey, Yadong Mu, Shih-Fu Chang, Michael. I. Jordan
Date: 2013-04-20 00:00:00
Tasks: Clustering, Event Detection, Face Recognition, Image Clustering, Motion Segmentation, Segmentation

Vision problems ranging from image clustering to motion segmentation to
semi-supervised learning can naturally be framed as subspace segmentation
problems, in which one aims to recover multiple low-dimensional subspaces from
noisy and corrupted input data. Low-Rank Representation (LRR), a convex
formulation of the subspace segmentation problem, is provably and empirically
accurate on small problems but does not scale to the massive sizes of modern
vision datasets. Moreover, past work aimed at scaling up low-rank matrix
factorization is not applicable to LRR given its non-decomposable constraints.
In this work, we propose a novel divide-and-conquer algorithm for large-scale
subspace segmentation that can cope with LRR's non-decomposable constraints and
maintains LRR's strong recovery guarantees. This has immediate implications for
the scalability of subspace segmentation, which we demonstrate on a benchmark
face recognition dataset and in simulations. We then introduce novel
applications of LRR-based subspace segmentation to large-scale semi-supervised
learning for multimedia event detection, concept detection, and image tagging.
In each case, we obtain state-of-the-art results and order-of-magnitude speed
ups.
On Measure Concentration of Random Maximum A-Posteriori Perturbations
Authors: Francesco Orabona, Tamir Hazan, Anand D. Sarwate, Tommi Jaakkola
Date: 2013-10-15 00:00:00

The maximum a-posteriori (MAP) perturbation framework has emerged as a useful
approach for inference and learning in high dimensional complex models. By
maximizing a randomly perturbed potential function, MAP perturbations generate
unbiased samples from the Gibbs distribution. Unfortunately, the computational
cost of generating so many high-dimensional random variables can be
prohibitive. More efficient algorithms use sequential sampling strategies based
on the expected value of low dimensional MAP perturbations. This paper develops
new measure concentration inequalities that bound the number of samples needed
to estimate such expected values. Applying the general result to MAP
perturbations can yield a more efficient algorithm to approximate sampling from
the Gibbs distribution. The measure concentration result is of general interest
and may be applicable to other areas involving expected estimations.
Adaptive Temporal Compressive Sensing for Video
Authors: Xin Yuan, Jianbo Yang, Patrick Llull, Xuejun Liao, Guillermo Sapiro, David J. Brady, Lawrence Carin
Date: 2013-02-14 00:00:00
Tasks: Compressive Sensing

This paper introduces the concept of adaptive temporal compressive sensing
(CS) for video. We propose a CS algorithm to adapt the compression ratio based
on the scene's temporal complexity, computed from the compressed data, without
compromising the quality of the reconstructed video. The temporal adaptivity is
manifested by manipulating the integration time of the camera, opening the
possibility to real-time implementation. The proposed algorithm is a
generalized temporal CS approach that can be incorporated with a diverse set of
existing hardware systems.
Exact Learning of RNA Energy Parameters From Structure
Authors: Hamidreza Chitsaz, Mohammad Aminisharifabad
Date: 2013-10-15 00:00:00

We consider the problem of exact learning of parameters of a linear RNA
energy model from secondary structure data. A necessary and sufficient
condition for learnability of parameters is derived, which is based on
computing the convex hull of union of translated Newton polytopes of input
sequences. The set of learned energy parameters is characterized as the convex
cone generated by the normal vectors to those facets of the resulting polytope
that are incident to the origin. In practice, the sufficient condition may not
be satisfied by the entire training data set; hence, computing a maximal subset
of training data for which the sufficient condition is satisfied is often
desired. We show that problem is NP-hard in general for an arbitrary
dimensional feature space. Using a randomized greedy algorithm, we select a
subset of RNA STRAND v2.0 database that satisfies the sufficient condition for
separate A-U, C-G, G-U base pair counting model. The set of learned energy
parameters includes experimentally measured energies of A-U, C-G, and G-U
pairs; hence, our parameter set is in agreement with the Turner parameters.
Optimal Sensor Placement and Enhanced Sparsity for Classification
Authors: B. W. Brunton, S. L. Brunton, J. L. Proctor, J. N. Kutz
Date: 2013-10-15 00:00:00
Tasks: Classification, Compressive Sensing, General Classification

The goal of compressive sensing is efficient reconstruction of data from few
measurements, sometimes leading to a categorical decision. If only
classification is required, reconstruction can be circumvented and the
measurements needed are orders-of-magnitude sparser still. We define enhanced
sparsity as the reduction in number of measurements required for classification
over reconstruction. In this work, we exploit enhanced sparsity and learn
spatial sensor locations that optimally inform a categorical decision. The
algorithm solves an l1-minimization to find the fewest entries of the full
measurement vector that exactly reconstruct the discriminant vector in feature
space. Once the sensor locations have been identified from the training data,
subsequent test samples are classified with remarkable efficiency, achieving
performance comparable to that obtained by discrimination using the full image.
Sensor locations may be learned from full images, or from a random subsample of
pixels. For classification between more than two categories, we introduce a
coupling parameter whose value tunes the number of sensors selected, trading
accuracy for economy. We demonstrate the algorithm on example datasets from
image recognition using PCA for feature extraction and LDA for discrimination;
however, the method can be broadly applied to non-image data and adapted to
work with other methods for feature extraction and discrimination.
Validation Rules for Assessing and Improving SKOS Mapping Quality
Authors: Hong Sun, Jos De Roo, Marc Twagirumukiza, Giovanni Mels, Kristof Depraetere, Boris De Vloed, Dirk Colaert
Date: 2013-10-15 00:00:00

The Simple Knowledge Organization System (SKOS) is popular for expressing
controlled vocabularies, such as taxonomies, classifications, etc., for their
use in Semantic Web applications. Using SKOS, concepts can be linked to other
concepts and organized into hierarchies inside a single terminology system.
Meanwhile, expressing mappings between concepts in different terminology
systems is also possible. This paper discusses potential quality issues in
using SKOS to express these terminology mappings. Problematic patterns are
defined and corresponding rules are developed to automatically detect
situations where the mappings either result in 'SKOS Vocabulary Hijacking' to
the source vocabularies or cause conflicts. An example of using the rules to
validate sample mappings between two clinical terminologies is given. The
validation rules, expressed in N3 format, are available as open source.
Sharp Inequalities for $f$-divergences
Authors: Adityanand Guntuboyina, Sujayam Saha, Geoffrey Schiebinger
Date: 2013-02-02 00:00:00

$f$-divergences are a general class of divergences between probability
measures which include as special cases many commonly used divergences in
probability, mathematical statistics and information theory such as
Kullback-Leibler divergence, chi-squared divergence, squared Hellinger
distance, total variation distance etc. In this paper, we study the problem of
maximizing or minimizing an $f$-divergence between two probability measures
subject to a finite number of constraints on other $f$-divergences. We show
that these infinite-dimensional optimization problems can all be reduced to
optimization problems over small finite dimensional spaces which are tractable.
Our results lead to a comprehensive and unified treatment of the problem of
obtaining sharp inequalities between $f$-divergences. We demonstrate that many
of the existing results on inequalities between $f$-divergences can be obtained
as special cases of our results and we also improve on some existing non-sharp
inequalities.
A Computational Model of Two Cognitive Transitions Underlying Cultural Evolution
Authors: Liane Gabora, Wei Wen Chia, Hadi Firouzi
Date: 2013-10-15 00:00:00
Tasks: Diversity, Vocal Bursts Valence Prediction

We tested the computational feasibility of the proposal that open-ended
cultural evolution was made possible by two cognitive transitions: (1) onset of
the capacity to chain thoughts together, followed by (2) onset of contextual
focus (CF): the capacity to shift between a divergent mode of thought conducive
to 'breaking out of a rut' and a convergent mode of thought conducive to minor
modifications. These transitions were simulated in EVOC, an agent-based model
of cultural evolution, in which the fitness of agents' actions increases as
agents invent ideas for new actions, and imitate the fittest of their
neighbors' actions. Both mean fitness and diversity of actions across the
society increased with chaining, and even more so with CF, as hypothesized. CF
was only effective when the fitness function changed, which supports its
hypothesized role in generating and refining ideas.
The BeiHang Keystroke Dynamics Authentication System
Authors: Juan Liu, Baochang Zhang, Linlin Shen, Jianzhuang Liu, Jason Zhao
Date: 2013-10-15 00:00:00
Tasks: General Classification

Keystroke Dynamics is an important biometric solution for person
authentication. Based upon keystroke dynamics, this paper designs an embedded
password protection device, develops an online system, collects two public
databases for promoting the research on keystroke authentication, exploits the
Gabor filter bank to characterize keystroke dynamics, and provides benchmark
results of three popular classification algorithms, one-class support vector
machine, Gaussian classifier, and nearest neighbour classifier.