pwc-archive/papers-with-abstracts huggingface dataset:
Exemplar-based Linear Discriminant Analysis for Robust Object Tracking
Authors: Changxin Gao, Feifei Chen, Jin-Gang Yu, Rui Huang, Nong Sang
Date: 2014-02-24 00:00:00
Tasks: Object, Object Tracking

Tracking-by-detection has become an attractive tracking technique, which
treats tracking as a category detection problem. However, the task in tracking
is to search for a specific object, rather than an object category as in
detection. In this paper, we propose a novel tracking framework based on
exemplar detector rather than category detector. The proposed tracker is an
ensemble of exemplar-based linear discriminant analysis (ELDA) detectors. Each
detector is quite specific and discriminative, because it is trained by a
single object instance and massive negatives. To improve its adaptivity, we
update both object and background models. Experimental results on several
challenging video sequences demonstrate the effectiveness and robustness of our
tracking algorithm.
Accelerating ABC methods using Gaussian processes
Authors: Richard D. Wilkinson
Date: 2014-01-07 00:00:00
Tasks: Gaussian Processes

Approximate Bayesian computation (ABC) methods are used to approximate
posterior distributions using simulation rather than likelihood calculations.
We introduce Gaussian process (GP) accelerated ABC, which we show can
significantly reduce the number of simulations required. As computational
resource is usually the main determinant of accuracy in ABC, GP-accelerated
methods can thus enable more accurate inference in some models. GP models of
the unknown log-likelihood function are used to exploit continuity and
smoothness, reducing the required computation. We use a sequence of models that
increase in accuracy, using intermediate models to rule out regions of the
parameter space as implausible. The methods will not be suitable for all
problems, but when they can be used, can result in significant computational
savings. For the Ricker model, we are able to achieve accurate approximations
to the posterior distribution using a factor of 100 fewer simulator evaluations
than comparable Monte Carlo approaches, and for a population genetics model we
are able to approximate the exact posterior for the first time.
To go deep or wide in learning?
Authors: Gaurav Pandey, Ambedkar Dukkipati
Date: 2014-02-23 00:00:00
Tasks: ARC

To achieve acceptable performance for AI tasks, one can either use
sophisticated feature extraction methods as the first layer in a two-layered
supervised learning model, or learn the features directly using a deep
(multi-layered) model. While the first approach is very problem-specific, the
second approach has computational overheads in learning multiple layers and
fine-tuning of the model. In this paper, we propose an approach called wide
learning based on arc-cosine kernels, that learns a single layer of infinite
width. We propose exact and inexact learning strategies for wide learning and
show that wide learning with single layer outperforms single layer as well as
deep architectures of finite width for some benchmark datasets.
Localization of License Plate Using Morphological Operations
Authors: V. Karthikeyan, V. J. Vijayalakshmi
Date: 2014-02-23 00:00:00

It is believed that there are currently millions of vehicles on the roads
worldwide. The over speed of vehicles,theft of vehicles, disobeying traffic
rules in public, an unauthorized person entering the restricted area are keep
on increasing. In order restrict against these criminal activities, we need an
automatic public security system. Each vehicle has their own Vehicle
Identification Number (VIN) as their primary identifier. The VIN is actually a
License Number which states a legal license to participate in the public
traffic. The proposed paper is to identify the vehicle with the help of
vehicles License Plate (LP).LPRS is one the most important part of the
Intelligent Transportation System (ITS) to locate the LP. In this paper certain
existing algorithm drawbacks are overcome by the proposed morphological
operations for LPRS. Morphological operation is chosen due to its higher
efficiency, noise filter capacity, accuracy, exact localization of LP and
speed.
A Novel Histogram Based Robust Image Registration Technique
Authors: V. Karthikeyan
Date: 2014-02-23 00:00:00
Tasks: Image Registration, Translation

In this paper, a method for Automatic Image Registration (AIR) through
histogram is proposed. Automatic image registration is one of the crucial steps
in the analysis of remotely sensed data. A new acquired image must be
transformed, using image registration techniques, to match the orientation and
scale of previous related images. This new approach combines several
segmentations of the pair of images to be registered. A relaxation parameter on
the histogram modes delineation is introduced. It is followed by
characterization of the extracted objects through the objects area, axis ratio,
and perimeter and fractal dimension. The matched objects are used for rotation
and translation estimation. It allows for the registration of pairs of images
with differences in rotation and translation. This method contributes to
subpixel accuracy.
Reciprocity in Gift-Exchange-Games
Authors: Rustam Tagiew, Dmitry I. Ignatov
Date: 2014-02-23 00:00:00

This paper presents an analysis of data from a gift-exchange-game experiment.
The experiment was described in `The Impact of Social Comparisons on
Reciprocity' by G\"achter et al. 2012. Since this paper uses state-of-art data
science techniques, the results provide a different point of view on the
problem. As already shown in relevant literature from experimental economics,
human decisions deviate from rational payoff maximization. The average gift
rate was $31$%. Gift rate was under no conditions zero. Further, we derive some
special findings and calculate their significance.
Path Thresholding: Asymptotically Tuning-Free High-Dimensional Sparse Regression
Authors: Divyanshu Vats, Richard G. Baraniuk
Date: 2014-02-23 00:00:00
Tasks: Model Selection, regression, Vocal Bursts Intensity Prediction

In this paper, we address the challenging problem of selecting tuning
parameters for high-dimensional sparse regression. We propose a simple and
computationally efficient method, called path thresholding (PaTh), that
transforms any tuning parameter-dependent sparse regression algorithm into an
asymptotically tuning-free sparse regression algorithm. More specifically, we
prove that, as the problem size becomes large (in the number of variables and
in the number of observations), PaTh performs accurate sparse regression, under
appropriate conditions, without specifying a tuning parameter. In
finite-dimensional settings, we demonstrate that PaTh can alleviate the
computational burden of model selection algorithms by significantly reducing
the search space of tuning parameters.
Semi-Supervised Nonlinear Distance Metric Learning via Forests of Max-Margin Cluster Hierarchies
Authors: David M. Johnson, Caiming Xiong, Jason J. Corso
Date: 2014-02-23 00:00:00
Tasks: Clustering, Image Retrieval, Metric Learning, Retrieval

Metric learning is a key problem for many data mining and machine learning
applications, and has long been dominated by Mahalanobis methods. Recent
advances in nonlinear metric learning have demonstrated the potential power of
non-Mahalanobis distance functions, particularly tree-based functions. We
propose a novel nonlinear metric learning method that uses an iterative,
hierarchical variant of semi-supervised max-margin clustering to construct a
forest of cluster hierarchies, where each individual hierarchy can be
interpreted as a weak metric over the data. By introducing randomness during
hierarchy training and combining the output of many of the resulting
semi-random weak hierarchy metrics, we can obtain a powerful and robust
nonlinear metric model. This method has two primary contributions: first, it is
semi-supervised, incorporating information from both constrained and
unconstrained points. Second, we take a relaxed approach to constraint
satisfaction, allowing the method to satisfy different subsets of the
constraints at different levels of the hierarchy rather than attempting to
simultaneously satisfy all of them. This leads to a more robust learning
algorithm. We compare our method to a number of state-of-the-art benchmarks on
$k$-nearest neighbor classification, large-scale image retrieval and
semi-supervised clustering problems, and find that our algorithm yields results
comparable or superior to the state-of-the-art, and is significantly more
robust to noise.
Swapping Variables for High-Dimensional Sparse Regression with Correlated Measurements
Authors: Divyanshu Vats, Richard G. Baraniuk
Date: 2013-12-05 00:00:00
Tasks: regression, Vocal Bursts Intensity Prediction

We consider the high-dimensional sparse linear regression problem of
accurately estimating a sparse vector using a small number of linear
measurements that are contaminated by noise. It is well known that the standard
cadre of computationally tractable sparse regression algorithms---such as the
Lasso, Orthogonal Matching Pursuit (OMP), and their extensions---perform poorly
when the measurement matrix contains highly correlated columns. To address this
shortcoming, we develop a simple greedy algorithm, called SWAP, that
iteratively swaps variables until convergence. SWAP is surprisingly effective
in handling measurement matrices with high correlations. In fact, we prove that
SWAP outputs the true support, the locations of the non-zero entries in the
sparse vector, under a relatively mild condition on the measurement matrix.
Furthermore, we show that SWAP can be used to boost the performance of any
sparse regression algorithm. We empirically demonstrate the advantages of SWAP
by comparing it with several state-of-the-art sparse regression algorithms.
A Supervised Goal Directed Algorithm in Economical Choice Behaviour: An Actor-Critic Approach
Authors: Keyvan Yahya
Date: 2013-12-20 00:00:00

This paper aims to find an algorithmic structure that affords to predict and explain economical choice behaviour particularly under uncertainty(random policies) by manipulating the prevalent Actor-Critic learning method to comply with the requirements we have been entrusted ever since the field of neuroeconomics dawned on us. Whilst skimming some basics of neuroeconomics that seem relevant to our discussion, we will try to outline some of the important works which have so far been done to simulate choice making processes. Concerning neurological findings that suggest the existence of two specific functions that are executed through Basal Ganglia all the way up to sub- cortical areas, namely 'rewards' and 'beliefs', we will offer a modified version of actor/critic algorithm to shed a light on the relation between these functions and most importantly resolve what is referred to as a challenge for actor-critic algorithms, that is, the lack of inheritance or hierarchy which avoids the system being evolved in continuous time tasks whence the convergence might not be emerged.
Efficient Semidefinite Spectral Clustering via Lagrange Duality
Authors: Yan Yan, Chunhua Shen, Hanzi Wang
Date: 2014-02-22 00:00:00
Tasks: Clustering

We propose an efficient approach to semidefinite spectral clustering (SSC),
which addresses the Frobenius normalization with the positive semidefinite
(p.s.d.) constraint for spectral clustering. Compared with the original
Frobenius norm approximation based algorithm, the proposed algorithm can more
accurately find the closest doubly stochastic approximation to the affinity
matrix by considering the p.s.d. constraint. In this paper, SSC is formulated
as a semidefinite programming (SDP) problem. In order to solve the high
computational complexity of SDP, we present a dual algorithm based on the
Lagrange dual formalization. Two versions of the proposed algorithm are
proffered: one with less memory usage and the other with faster convergence
rate. The proposed algorithm has much lower time complexity than that of the
standard interior-point based SDP solvers. Experimental results on both UCI
data sets and real-world image data sets demonstrate that 1) compared with the
state-of-the-art spectral clustering methods, the proposed algorithm achieves
better clustering performance; and 2) our algorithm is much more efficient and
can solve larger-scale SSC problems than those standard interior-point SDP
solvers.
Class Proportion Estimation with Application to Multiclass Anomaly Rejection
Authors: Tyler Sanderson, Clayton Scott
Date: 2013-06-21 00:00:00
Tasks: Domain Adaptation

This work addresses two classification problems that fall under the heading
of domain adaptation, wherein the distributions of training and testing
examples differ. The first problem studied is that of class proportion
estimation, which is the problem of estimating the class proportions in an
unlabeled testing data set given labeled examples of each class. Compared to
previous work on this problem, our approach has the novel feature that it does
not require labeled training data from one of the classes. This property allows
us to address the second domain adaptation problem, namely, multiclass anomaly
rejection. Here, the goal is to design a classifier that has the option of
assigning a "reject" label, indicating that the instance did not arise from a
class present in the training data. We establish consistent learning strategies
for both of these domain adaptation problems, which to our knowledge are the
first of their kind. We also implement the class proportion estimation
technique and demonstrate its performance on several benchmark data sets.
Scaling Nonparametric Bayesian Inference via Subsample-Annealing
Authors: Fritz Obermeyer, Jonathan Glidden, Eric Jonas
Date: 2014-02-22 00:00:00
Tasks: Bayesian Inference, Clustering, Nonparametric Clustering

We describe an adaptation of the simulated annealing algorithm to
nonparametric clustering and related probabilistic models. This new algorithm
learns nonparametric latent structure over a growing and constantly churning
subsample of training data, where the portion of data subsampled can be
interpreted as the inverse temperature beta(t) in an annealing schedule. Gibbs
sampling at high temperature (i.e., with a very small subsample) can more
quickly explore sketches of the final latent state by (a) making longer jumps
around latent space (as in block Gibbs) and (b) lowering energy barriers (as in
simulated annealing). We prove subsample annealing speeds up mixing time N^2 ->
N in a simple clustering model and exp(N) -> N in another class of models,
where N is data size. Empirically subsample-annealing outperforms naive Gibbs
sampling in accuracy-per-wallclock time, and can scale to larger datasets and
deeper hierarchical models. We demonstrate improved inference on million-row
subsamples of US Census data and network log data and a 307-row hospital rating
dataset, using a Pitman-Yor generalization of the Cross Categorization model.
Information Aggregation in Exponential Family Markets
Authors: Jacob Abernethy, Sindhu Kutty, Sébastien Lahaie, Rahul Sami
Date: 2014-02-22 00:00:00

We consider the design of prediction market mechanisms known as automated
market makers. We show that we can design these mechanisms via the mold of
\emph{exponential family distributions}, a popular and well-studied probability
distribution template used in statistics. We give a full development of this
relationship and explore a range of benefits. We draw connections between the
information aggregation of market prices and the belief aggregation of learning
agents that rely on exponential family distributions. We develop a very natural
analysis of the market behavior as well as the price equilibrium under the
assumption that the traders exhibit risk aversion according to exponential
utility. We also consider similar aspects under alternative models, such as
when traders are budget constrained.
Le Cam meets LeCun: Deficiency and Generic Feature Learning
Authors: Brendan van Rooyen, Robert C. Williamson
Date: 2014-02-20 00:00:00
Tasks: Deep Learning

"Deep Learning" methods attempt to learn generic features in an unsupervised
fashion from a large unlabelled data set. These generic features should perform
as well as the best hand crafted features for any learning problem that makes
use of this data. We provide a definition of generic features, characterize
when it is possible to learn them and provide methods closely related to the
autoencoder and deep belief network of deep learning. In order to do so we use
the notion of deficiency and illustrate its value in studying certain general
learning problems.
Characterizing and computing stable models of logic programs: The non-stratified case
Authors: Gianpaolo Brignoli, Stefania Costantini, Ottavio D'Antona, Alessandro Provetti
Date: 2014-02-21 00:00:00

Stable Logic Programming (SLP) is an emergent, alternative style of logic
programming: each solution to a problem is represented by a stable model of a
deductive database/function-free logic program encoding the problem itself.
Several implementations now exist for stable logic programming, and their
performance is rapidly improving. To make SLP generally applicable, it should
be possible to check for consistency (i.e., existence of stable models) of the
input program before attempting to answer queries. In the literature, only
rather strong sufficient conditions have been proposed for consistency, e.g.,
stratification. This paper extends these results in several directions. First,
the syntactic features of programs, viz. cyclic negative dependencies,
affecting the existence of stable models are characterized, and their relevance
is discussed. Next, a new graph representation of logic programs, the Extended
Dependency Graph (EDG), is introduced, which conveys enough information for
reasoning about stable models (while the traditional Dependency Graph does
not). Finally, we show that the problem of the existence of stable models can
be reformulated in terms of coloring of the EDG.
An Evolutionary approach for solving Shrödinger Equation
Authors: Khalid jebari, Mohammed Madiafi, Abdelaziz Elmoujahid
Date: 2014-02-21 00:00:00

The purpose of this paper is to present a method of solving the Shr\"odinger
Equation (SE) by Genetic Algorithms and Grammatical Evolution. The method forms
generations of trial solutions expressed in an analytical form. We illustrate
the effectiveness of this method providing, for example, the results of its
application to a quantum system minimal energy, and we compare these results
with those produced by traditional analytical methods
Recovering the Optimal Solution by Dual Random Projection
Authors: Lijun Zhang, Mehrdad Mahdavi, Rong Jin, Tianbao Yang, Shenghuo Zhu
Date: 2012-11-13 00:00:00
Tasks: General Classification

Random projection has been widely used in data classification. It maps
high-dimensional data into a low-dimensional subspace in order to reduce the
computational cost in solving the related optimization problem. While previous
studies are focused on analyzing the classification performance of using random
projection, in this work, we consider the recovery problem, i.e., how to
accurately recover the optimal solution to the original optimization problem in
the high-dimensional space based on the solution learned from the subspace
spanned by random projections. We present a simple algorithm, termed Dual
Random Projection, that uses the dual solution of the low-dimensional
optimization problem to recover the optimal solution to the original problem.
Our theoretical analysis shows that with a high probability, the proposed
algorithm is able to accurately recover the optimal solution to the original
problem, provided that the data matrix is of low rank or can be well
approximated by a low rank matrix.
Important Molecular Descriptors Selection Using Self Tuned Reweighted Sampling Method for Prediction of Antituberculosis Activity
Authors: Doreswamy, Chanabasayya . M. Vastrad
Date: 2014-02-21 00:00:00
Tasks: regression, STS

In this paper, a new descriptor selection method for selecting an optimal
combination of important descriptors of sulfonamide derivatives data, named
self tuned reweighted sampling (STRS), is developed. descriptors are defined as
the descriptors with large absolute coefficients in a multivariate linear
regression model such as partial least squares(PLS). In this study, the
absolute values of regression coefficients of PLS model are used as an index
for evaluating the importance of each descriptor Then, based on the importance
level of each descriptor, STRS sequentially selects N subsets of descriptors
from N Monte Carlo (MC) sampling runs in an iterative and competitive manner.
In each sampling run, a fixed ratio (e.g. 80%) of samples is first randomly
selected to establish a regresson model. Next, based on the regression
coefficients, a two-step procedure including rapidly decreasing function (RDF)
based enforced descriptor selection and self tuned sampling (STS) based
competitive descriptor selection is adopted to select the important
descriptorss. After running the loops, a number of subsets of descriptors are
obtained and root mean squared error of cross validation (RMSECV) of PLS models
established with subsets of descriptors is computed. The subset of descriptors
with the lowest RMSECV is considered as the optimal descriptor subset. The
performance of the proposed algorithm is evaluated by sulfanomide derivative
dataset. The results reveal an good characteristic of STRS that it can usually
locate an optimal combination of some important descriptors which are
interpretable to the biologically of interest. Additionally, our study shows
that better prediction is obtained by STRS when compared to full descriptor set
PLS modeling, Monte Carlo uninformative variable elimination (MC-UVE).
Extended Breadth-First Search Algorithm
Authors: Tamás Kádek, János Pánovics
Date: 2014-02-21 00:00:00

The task of artificial intelligence is to provide representation techniques
for describing problems, as well as search algorithms that can be used to
answer our questions. A widespread and elaborated model is state-space
representation, which, however, has some shortcomings. Classical search
algorithms are not applicable in practice when the state space contains even
only a few tens of thousands of states. We can give remedy to this problem by
defining some kind of heuristic knowledge. In case of classical state-space
representation, heuristic must be defined so that it qualifies an arbitrary
state based on its "goodness," which is obviously not trivial. In our paper, we
introduce an algorithm that gives us the ability to handle huge state spaces
and to use a heuristic concept which is easier to embed into search algorithms.
Exploiting Two-Dimensional Group Sparsity in 1-Bit Compressive Sensing
Authors: Xiangrong Zeng, Mário A. T. Figueiredo
Date: 2014-02-20 00:00:00
Tasks: Compressive Sensing, Vocal Bursts Valence Prediction

We propose a new approach, {\it two-dimensional fused binary compressive
sensing} (2DFBCS) to recover 2D sparse piece-wise signals from 1-bit
measurements, exploiting 2D group sparsity for 1-bit compressive sensing
recovery. The proposed method is a modified 2D version of the previous {\it
binary iterative hard thresholding} (2DBIHT) algorithm, where the objective
function includes a 2D one-sided $\ell_1$ (or $\ell_2$) penalty function
encouraging agreement with the observed data, an indicator function of
$K$-sparsity, and a total variation (TV) or modified TV (MTV) constraint. The
subgradient of the 2D one-sided $\ell_1$ (or $\ell_2$) penalty and the
projection onto the $K$-sparsity and TV or MTV constraint can be computed
efficiently, allowing the appliaction of algorithms of the {\it
forward-backward splitting} (a.k.a. {\it iterative shrinkage-thresholding})
family. Experiments on the recovery of 2D sparse piece-wise smooth signals show
that the proposed approach is able to take advantage of the piece-wise
smoothness of the original signal, achieving more accurate recovery than
2DBIHT. More specifically, 2DFBCS with the MTV and the $\ell_2$ penalty
performs best amongst the algorithms tested.