pwc-archive/papers-with-abstracts huggingface dataset:
The BOSARIS Toolkit: Theory, Algorithms and Code for Surviving the New DCF
Authors: Niko Brümmer, Edward de Villiers
Date: 2013-04-10 00:00:00
Tasks: CPU

The change of two orders of magnitude in the 'new DCF' of NIST's SRE'10,
relative to the 'old DCF' evaluation criterion, posed a difficult challenge for
participants and evaluator alike. Initially, participants were at a loss as to
how to calibrate their systems, while the evaluator underestimated the required
number of evaluation trials. After the fact, it is now obvious that both
calibration and evaluation require very large sets of trials. This poses the
challenges of (i) how to decide what number of trials is enough, and (ii) how
to process such large data sets with reasonable memory and CPU requirements.
After SRE'10, at the BOSARIS Workshop, we built solutions to these problems
into the freely available BOSARIS Toolkit. This paper explains the principles
and algorithms behind this toolkit. The main contributions of the toolkit are:
1. The Normalized Bayes Error-Rate Plot, which analyses likelihood- ratio
calibration over a wide range of DCF operating points. These plots also help in
judging the adequacy of the sizes of calibration and evaluation databases. 2.
Efficient algorithms to compute DCF and minDCF for large score files, over the
range of operating points required by these plots. 3. A new score file format,
which facilitates working with very large trial lists. 4. A faster logistic
regression optimizer for fusion and calibration. 5. A principled way to define
EER (equal error rate), which is of practical interest when the absolute error
count is small.
Predictive Correlation Screening: Application to Two-stage Predictor Design in High Dimension
Authors: Hamed Firouzi, Bala Rajaratnam, Alfred Hero
Date: 2013-03-10 00:00:00
Tasks: Variable Selection

We introduce a new approach to variable selection, called Predictive
Correlation Screening, for predictor design. Predictive Correlation Screening
(PCS) implements false positive control on the selected variables, is well
suited to small sample sizes, and is scalable to high dimensions. We establish
asymptotic bounds for Familywise Error Rate (FWER), and resultant mean square
error of a linear predictor on the selected variables. We apply Predictive
Correlation Screening to the following two-stage predictor design problem. An
experimenter wants to learn a multivariate predictor of gene expressions based
on successive biological samples assayed on mRNA arrays. She assays the whole
genome on a few samples and from these assays she selects a small number of
variables using Predictive Correlation Screening. To reduce assay cost, she
subsequently assays only the selected variables on the remaining samples, to
learn the predictor coefficients. We show superiority of Predictive Correlation
Screening relative to LASSO and correlation learning (sometimes popularly
referred to in the literature as marginal regression or simple thresholding) in
terms of performance and computational complexity.
Symmetry-Aware Marginal Density Estimation
Authors: Mathias Niepert
Date: 2013-04-09 00:00:00
Tasks: Density Estimation

The Rao-Blackwell theorem is utilized to analyze and improve the scalability
of inference in large probabilistic models that exhibit symmetries. A novel
marginal density estimator is introduced and shown both analytically and
empirically to outperform standard estimators by several orders of magnitude.
The developed theory and algorithms apply to a broad class of probabilistic
models including statistical relational models considered not susceptible to
lifted probabilistic inference.
Image Classification by Feature Dimension Reduction and Graph based Ranking
Authors: Yao Nan, Qian Feng, Sun Zuolei
Date: 2013-04-09 00:00:00
Tasks: Classification, Dimensionality Reduction, General Classification, image-classification, Image Classification, Image Retrieval, Retrieval

Dimensionality reduction (DR) of image features plays an important role in
image retrieval and classification tasks. Recently, two types of methods have
been proposed to improve the both the accuracy and efficiency for the
dimensionality reduction problem. One uses Non-negative matrix factorization
(NMF) to describe the image distribution on the space of base matrix. Another
one for dimension reduction trains a subspace projection matrix to project
original data space into some low-dimensional subspaces which have deep
architecture, so that the low-dimensional codes would be learned. At the same
time, the graph based similarity learning algorithm which tries to exploit
contextual information for improving the effectiveness of image rankings is
also proposed for image class and retrieval problem. In this paper, after above
two methods mentioned are utilized to reduce the high-dimensional features of
images respectively, we learn the graph based similarity for the image
classification problem. This paper compares the proposed approach with other
approaches on an image database.
Image Retrieval using Histogram Factorization and Contextual Similarity Learning
Authors: Liu Liang
Date: 2013-04-07 00:00:00
Tasks: Content-Based Image Retrieval, Image Retrieval, Retrieval

Image retrieval has been a top topic in the field of both computer vision and
machine learning for a long time. Content based image retrieval, which tries to
retrieve images from a database visually similar to a query image, has
attracted much attention. Two most important issues of image retrieval are the
representation and ranking of the images. Recently, bag-of-words based method
has shown its power as a representation method. Moreover, nonnegative matrix
factorization is also a popular way to represent the data samples. In addition,
contextual similarity learning has also been studied and proven to be an
effective method for the ranking problem. However, these technologies have
never been used together. In this paper, we developed an effective image
retrieval system by representing each image using the bag-of-words method as
histograms, and then apply the nonnegative matrix factorization to factorize
the histograms, and finally learn the ranking score using the contextual
similarity learning method. The proposed novel system is evaluated on a large
scale image database and the effectiveness is shown.
For Solving Linear Equations Recombination is a Needless Operation in Time-Variant Adaptive Hybrid Algorithms
Authors: A. R. M. Jalal Uddin Jamali, Mohammad Arif Hossain, G. M. Moniruzzaman, M. M. A. Hashem
Date: 2013-04-09 00:00:00
Tasks: Evolutionary Algorithms

Recently hybrid evolutionary computation (EC) techniques are successfully
implemented for solving large sets of linear equations. All the recently
developed hybrid evolutionary algorithms, for solving linear equations, contain
both the recombination and the mutation operations. In this paper, two modified
hybrid evolutionary algorithms contained time-variant adaptive evolutionary
technique are proposed for solving linear equations in which recombination
operation is absent. The effectiveness of the recombination operator has been
studied for the time-variant adaptive hybrid algorithms for solving large set
of linear equations. Several experiments have been carried out using both the
proposed modified hybrid evolutionary algorithms (in which the recombination
operation is absent) and corresponding existing hybrid algorithms (in which the
recombination operation is present) to solve large set of linear equations. It
is found that the number of generations required by the existing hybrid
algorithms (i.e. the Gauss-Seidel-SR based time variant adaptive (GSBTVA)
hybrid algorithm and the Jacobi-SR based time variant adaptive (JBTVA) hybrid
algorithm) and modified hybrid algorithms (i.e. the modified Gauss-Seidel-SR
based time variant adaptive (MGSBTVA) hybrid algorithm and the modified
Jacobi-SR based time variant adaptive (MJBTVA) hybrid algorithm) are
comparable. Also the proposed modified algorithms require less amount of
computational time in comparison to the corresponding existing hybrid
algorithms. As the proposed modified hybrid algorithms do not contain
recombination operation, so they require less computational effort, and also
they are more efficient, effective and easy to implement.
A New Distributed Evolutionary Computation Technique for Multi-Objective Optimization
Authors: Md. Asadul Islam, G. M. Mashrur-E-Elahi, M. M. A. Hashem
Date: 2013-04-09 00:00:00
Tasks: Evolutionary Algorithms

Now-a-days, it is important to find out solutions of Multi-Objective
Optimization Problems (MOPs). Evolutionary Strategy helps to solve such real
world problems efficiently and quickly. But sequential Evolutionary Algorithms
(EAs) require an enormous computation power to solve such problems and it takes
much time to solve large problems. To enhance the performance for solving this
type of problems, this paper presents a new Distributed Novel Evolutionary
Strategy Algorithm (DNESA) for Multi-Objective Optimization. The proposed DNESA
applies the divide-and-conquer approach to decompose population into smaller
sub-population and involves multiple solutions in the form of cooperative
sub-populations. In DNESA, the server distributes the total computation load to
all associate clients and simulation results show that the time for solving
large problems is much less than sequential EAs. Also DNESA shows better
performance in convergence test when compared with other three well-known EAs.
On Appropriate Selection of Fuzzy Aggregation Operators in Medical Decision Support System
Authors: K. M. Motahar Hossain, Zahir Raihan, M. M. A. Hashem
Date: 2013-04-09 00:00:00
Tasks: Decision Making

The Decision Support System (DSS) contains more than one antecedent and the
degrees of strength of the antecedents need to be combined to determine the
overall strength of the rule consequent. The membership values of the
linguistic variables in Fuzzy have to be combined using an aggregation
operator. But it is not feasible to predefine the form of aggregation operators
in decision making. Instead, each rule should be found based on the feeling of
the experts and on their actual decision pattern over the set of typical
examples. Thus this work illustrates how the choice of aggregation operators is
intended to mimic human decision making and can be selected and adjusted to fit
empirical data, a series of test cases. Both parametrized and nonparametrized
aggregation operators are adapted to fit empirical data. Moreover, they
provided compensatory properties and, therefore, seemed to produce a better
decision support system. To solve the problem, a threshold point from the
output of the aggregation operators is chosen as the separation point between
two classes. The best achieved accuracy is chosen as the appropriate
aggregation operator. Thus a medical decision can be generated which is very
close to a practitioner's guideline.
Kernel Reconstruction ICA for Sparse Representation
Authors: Yanhui Xiao, Zhenfeng Zhu, Yao Zhao
Date: 2013-04-09 00:00:00
Tasks: image-classification, Image Classification

Independent Component Analysis (ICA) is an effective unsupervised tool to
learn statistically independent representation. However, ICA is not only
sensitive to whitening but also difficult to learn an over-complete basis.
Consequently, ICA with soft Reconstruction cost(RICA) was presented to learn
sparse representations with over-complete basis even on unwhitened data.
Whereas RICA is infeasible to represent the data with nonlinear structure due
to its intrinsic linearity. In addition, RICA is essentially an unsupervised
method and can not utilize the class information. In this paper, we propose a
kernel ICA model with reconstruction constraint (kRICA) to capture the
nonlinear features. To bring in the class information, we further extend the
unsupervised kRICA to a supervised one by introducing a discrimination
constraint, namely d-kRICA. This constraint leads to learn a structured basis
consisted of basis vectors from different basis subsets corresponding to
different class labels. Then each subset will sparsely represent well for its
own class but not for the others. Furthermore, data samples belonging to the
same class will have similar representations, and thereby the learned sparse
representations can take more discriminative power. Experimental results
validate the effectiveness of kRICA and d-kRICA for image classification.
Corpus-based Web Document Summarization using Statistical and Linguistic Approach
Authors: Rushdi Shams, M. M. A. Hashem, Afrina Hossain, Suraiya Rumana Akter, Monika Gope
Date: 2013-04-09 00:00:00
Tasks: Document Summarization, Sentence

Single document summarization generates summary by extracting the
representative sentences from the document. In this paper, we presented a novel
technique for summarization of domain-specific text from a single web document
that uses statistical and linguistic analysis on the text in a reference corpus
and the web document. The proposed summarizer uses the combinational function
of Sentence Weight (SW) and Subject Weight (SuW) to determine the rank of a
sentence, where SW is the function of number of terms (t_n) and number of words
(w_n) in a sentence, and term frequency (t_f) in the corpus and SuW is the
function of t_n and w_n in a subject, and t_f in the corpus. 30 percent of the
ranked sentences are considered to be the summary of the web document. We
generated three web document summaries using our technique and compared each of
them with the summaries developed manually from 16 different human subjects.
Results showed that 68 percent of the summaries produced by our approach
satisfy the manual summaries.
Evolutionary Design of Digital Circuits Using Genetic Programming
Authors: S. M. Ashik Eftekhar, Sk. Mahbub Habib, M. M. A. Hashem
Date: 2013-04-09 00:00:00

For simple digital circuits, conventional method of designing circuits can
easily be applied. But for complex digital circuits, the conventional method of
designing circuits is not fruitfully applicable because it is time-consuming.
On the contrary, Genetic Programming is used mostly for automatic program
generation. The modern approach for designing Arithmetic circuits, commonly
digital circuits, is based on Graphs. This graph-based evolutionary design of
arithmetic circuits is a method of optimized designing of arithmetic circuits.
In this paper, a new technique for evolutionary design of digital circuits is
proposed using Genetic Programming (GP) with Subtree Mutation in place of
Graph-based design. The results obtained using this technique demonstrates the
potential capability of genetic programming in digital circuit design with
limited computer algorithms. The proposed technique, helps to simplify and
speed up the process of designing digital circuits, discovers a variation in
the field of digital circuit design where optimized digital circuits can be
successfully and effectively designed.
Convergence of latent mixing measures in finite and infinite mixture models
Authors: XuanLong Nguyen
Date: 2011-09-15 00:00:00
Tasks: Clustering

This paper studies convergence behavior of latent mixing measures that arise
in finite and infinite mixture models, using transportation distances (i.e.,
Wasserstein metrics). The relationship between Wasserstein distances on the
space of mixing measures and f-divergence functionals such as Hellinger and
Kullback-Leibler distances on the space of mixture distributions is
investigated in detail using various identifiability conditions. Convergence in
Wasserstein metrics for discrete measures implies convergence of individual
atoms that provide support for the measures, thereby providing a natural
interpretation of convergence of clusters in clustering applications where
mixture models are typically employed. Convergence rates of posterior
distributions for latent mixing measures are established, for both finite
mixtures of multivariate distributions and infinite mixtures based on the
Dirichlet process.
Efficient Learning of Domain-invariant Image Representations
Authors: Judy Hoffman, Erik Rodner, Jeff Donahue, Trevor Darrell, Kate Saenko
Date: 2013-01-15 00:00:00
Tasks: Representation Learning

We present an algorithm that learns representations which explicitly
compensate for domain mismatch and which can be efficiently realized as linear
classifiers. Specifically, we form a linear transformation that maps features
from the target (test) domain to the source (training) domain as part of
training the classifier. We optimize both the transformation and classifier
parameters jointly, and introduce an efficient cost function based on
misclassification loss. Our method combines several features previously
unavailable in a single algorithm: multi-class adaptation through
representation learning, ability to map across heterogeneous feature spaces,
and scalability to large datasets. We present experiments on several image
datasets that demonstrate improved accuracy and computational advantages
compared to previous approaches.
The PAV algorithm optimizes binary proper scoring rules
Authors: Niko Brummer, Johan du Preez
Date: 2013-04-08 00:00:00

There has been much recent interest in application of the
pool-adjacent-violators (PAV) algorithm for the purpose of calibrating the
probabilistic outputs of automatic pattern recognition and machine learning
algorithms. Special cost functions, known as proper scoring rules form natural
objective functions to judge the goodness of such calibration. We show that for
binary pattern classifiers, the non-parametric optimization of calibration,
subject to a monotonicity constraint, can be solved by PAV and that this
solution is optimal for all regular binary proper scoring rules. This extends
previous results which were limited to convex binary proper scoring rules. We
further show that this result holds not only for calibration of probabilities,
but also for calibration of log-likelihood-ratios, in which case optimality
holds independently of the prior probabilities of the pattern classes.
ClusterCluster: Parallel Markov Chain Monte Carlo for Dirichlet Process Mixtures
Authors: Dan Lovell, Jonathan Malmaud, Ryan P. Adams, Vikash K. Mansinghka
Date: 2013-04-08 00:00:00
Tasks: Density Estimation, Time Series, Time Series Analysis

The Dirichlet process (DP) is a fundamental mathematical tool for Bayesian
nonparametric modeling, and is widely used in tasks such as density estimation,
natural language processing, and time series modeling. Although MCMC inference
methods for the DP often provide a gold standard in terms asymptotic accuracy,
they can be computationally expensive and are not obviously parallelizable. We
propose a reparameterization of the Dirichlet process that induces conditional
independencies between the atoms that form the random measure. This conditional
independence enables many of the Markov chain transition operators for DP
inference to be simulated in parallel across multiple cores. Applied to mixture
modeling, our approach enables the Dirichlet process to simultaneously learn
clusters that describe the data and superclusters that define the granularity
of parallelization. Unlike previous approaches, our technique does not require
alteration of the model and leaves the true posterior distribution invariant.
It also naturally lends itself to a distributed software implementation in
terms of Map-Reduce, which we test in cluster configurations of over 50
machines and 100 cores. We present experiments exploring the parallel
efficiency and convergence properties of our approach on both synthetic and
real-world data, including runs on 1MM data vectors in 256 dimensions.
Synaptic Scaling Balances Learning in a Spiking Model of Neocortex
Authors: Mark Rowan, Samuel Neymotin
Date: 2013-04-08 00:00:00

Learning in the brain requires complementary mechanisms: potentiation and
activity-dependent homeostatic scaling. We introduce synaptic scaling to a
biologically-realistic spiking model of neocortex which can learn changes in
oscillatory rhythms using STDP, and show that scaling is necessary to balance
both positive and negative changes in input from potentiation and atrophy. We
discuss some of the issues that arise when considering synaptic scaling in such
a model, and show that scaling regulates activity whilst allowing learning to
remain unaltered.
Relevance As a Metric for Evaluating Machine Learning Algorithms
Authors: Aravind Kota Gopalakrishna, Tanir Ozcelebi, Antonio Liotta, Johan J. Lukkien
Date: 2013-03-28 00:00:00
Tasks: BIG-bench Machine Learning, General Classification

In machine learning, the choice of a learning algorithm that is suitable for
the application domain is critical. The performance metric used to compare
different algorithms must also reflect the concerns of users in the application
domain under consideration. In this work, we propose a novel probability-based
performance metric called Relevance Score for evaluating supervised learning
algorithms. We evaluate the proposed metric through empirical analysis on a
dataset gathered from an intelligent lighting pilot installation. In comparison
to the commonly used Classification Accuracy metric, the Relevance Score proves
to be more appropriate for a certain class of applications.
Geometric tree kernels: Classification of COPD from airway tree geometry
Authors: Aasa Feragen, Jens Petersen, Dominik Grimm, Asger Dirksen, Jesper Holst Pedersen, Karsten Borgwardt, Marleen de Bruijne
Date: 2013-03-29 00:00:00
Tasks: Classification, General Classification, Two-sample testing

Methodological contributions: This paper introduces a family of kernels for
analyzing (anatomical) trees endowed with vector valued measurements made along
the tree. While state-of-the-art graph and tree kernels use combinatorial
tree/graph structure with discrete node and edge labels, the kernels presented
in this paper can include geometric information such as branch shape, branch
radius or other vector valued properties. In addition to being flexible in
their ability to model different types of attributes, the presented kernels are
computationally efficient and some of them can easily be computed for large
datasets (N of the order 10.000) of trees with 30-600 branches. Combining the
kernels with standard machine learning tools enables us to analyze the relation
between disease and anatomical tree structure and geometry. Experimental
results: The kernels are used to compare airway trees segmented from low-dose
CT, endowed with branch shape descriptors and airway wall area percentage
measurements made along the tree. Using kernelized hypothesis testing we show
that the geometric airway trees are significantly differently distributed in
patients with Chronic Obstructive Pulmonary Disease (COPD) than in healthy
individuals. The geometric tree kernels also give a significant increase in the
classification accuracy of COPD from geometric tree structure endowed with
airway wall thickness measurements in comparison with state-of-the-art methods,
giving further insight into the relationship between airway wall thickness and
COPD. Software: Software for computing kernels and statistical tests is
available at http://image.diku.dk/aasa/software.php.
Computing Datalog Rewritings beyond Horn Ontologies
Authors: Bernardo Cuenca Grau, Boris Motik, Giorgos Stoilos, Ian Horrocks
Date: 2013-04-04 00:00:00

Rewriting-based approaches for answering queries over an OWL 2 DL ontology
have so far been developed mainly for Horn fragments of OWL 2 DL. In this
paper, we study the possibilities of answering queries over non-Horn ontologies
using datalog rewritings. We prove that this is impossible in general even for
very simple ontology languages, and even if PTIME = NP. Furthermore, we present
a resolution-based procedure for $\SHI$ ontologies that, in case it terminates,
produces a datalog rewriting of the ontology. Our procedure necessarily
terminates on DL-Lite_{bool}^H ontologies---an extension of OWL 2 QL with
transitive roles and Boolean connectives.