pwc-archive/papers-with-abstracts huggingface dataset:
Entropic one-class classifiers
Authors: Lorenzo Livi, Alireza Sadeghian, Witold Pedrycz
Date: 2014-07-28 00:00:00
Tasks: Anomaly Detection, Benchmarking, General Classification, global-optimization, One-Class Classification, One-class classifier

The one-class classification problem is a well-known research endeavor in
pattern recognition. The problem is also known under different names, such as
outlier and novelty/anomaly detection. The core of the problem consists in
modeling and recognizing patterns belonging only to a so-called target class.
All other patterns are termed non-target, and therefore they should be
recognized as such. In this paper, we propose a novel one-class classification
system that is based on an interplay of different techniques. Primarily, we
follow a dissimilarity representation based approach; we embed the input data
into the dissimilarity space by means of an appropriate parametric
dissimilarity measure. This step allows us to process virtually any type of
data. The dissimilarity vectors are then represented through a weighted
Euclidean graphs, which we use to (i) determine the entropy of the data
distribution in the dissimilarity space, and at the same time (ii) derive
effective decision regions that are modeled as clusters of vertices. Since the
dissimilarity measure for the input data is parametric, we optimize its
parameters by means of a global optimization scheme, which considers both
mesoscopic and structural characteristics of the data represented through the
graphs. The proposed one-class classifier is designed to provide both hard
(Boolean) and soft decisions about the recognition of test patterns, allowing
an accurate description of the classification process. We evaluate the
performance of the system on different benchmarking datasets, containing either
feature-based or structured patterns. Experimental results demonstrate the
effectiveness of the proposed technique.
Fast and optimal nonparametric sequential design for astronomical observations
Authors: Justin J. Yang, Xufei Wang, Pavlos Protopapas, Luke Bornn
Date: 2015-01-11 00:00:00
Tasks: Astronomy, Experimental Design

The spectral energy distribution (SED) is a relatively easy way for
astronomers to distinguish between different astronomical objects such as
galaxies, black holes, and stellar objects. By comparing the observations from
a source at different frequencies with template models, astronomers are able to
infer the type of this observed object. In this paper, we take a Bayesian model
averaging perspective to learn astronomical objects, employing a Bayesian
nonparametric approach to accommodate the deviation from convex combinations of
known log-SEDs. To effectively use telescope time for observations, we then
study Bayesian nonparametric sequential experimental design without conjugacy,
in which we use sequential Monte Carlo as an efficient tool to maximize the
volume of information stored in the posterior distribution of the parameters of
interest. A new technique for performing inferences in log-Gaussian Cox
processes called the Poisson log-normal approximation is also proposed.
Simulations show the speed, accuracy, and usefulness of our method. While the
strategy we propose in this paper is brand new in the astronomy literature, the
inferential techniques developed apply to more general nonparametric sequential
experimental design problems.
Learning a Fuzzy Hyperplane Fat Margin Classifier with Minimum VC dimension
Authors: Jayadeva, Sanjit Singh Batra, Siddarth Sabharwal
Date: 2015-01-11 00:00:00

The Vapnik-Chervonenkis (VC) dimension measures the complexity of a learning
machine, and a low VC dimension leads to good generalization. The recently
proposed Minimal Complexity Machine (MCM) learns a hyperplane classifier by
minimizing an exact bound on the VC dimension. This paper extends the MCM
classifier to the fuzzy domain. The use of a fuzzy membership is known to
reduce the effect of outliers, and to reduce the effect of noise on learning.
Experimental results show, that on a number of benchmark datasets, the the
fuzzy MCM classifier outperforms SVMs and the conventional MCM in terms of
generalization, and that the fuzzy MCM uses fewer support vectors. On several
benchmark datasets, the fuzzy MCM classifier yields excellent test set
accuracies while using one-tenth the number of support vectors used by SVMs.
A Gaussian Particle Filter Approach for Sensors to Track Multiple Moving Targets
Authors: Haojun Li
Date: 2015-01-11 00:00:00

In a variety of problems, the number and state of multiple moving targets are
unknown and are subject to be inferred from their measurements obtained by a
sensor with limited sensing ability. This type of problems is raised in a
variety of applications, including monitoring of endangered species, cleaning,
and surveillance. Particle filters are widely used to estimate target state
from its prior information and its measurements that recently become available,
especially for the cases when the measurement model and the prior distribution
of state of interest are non-Gaussian. However, the problem of estimating
number of total targets and their state becomes intractable when the number of
total targets and the measurement-target association are unknown. This paper
presents a novel Gaussian particle filter technique that combines Kalman filter
and particle filter for estimating the number and state of total targets based
on the measurement obtained online. The estimation is represented by a set of
weighted particles, different from classical particle filter, where each
particle is a Gaussian distribution instead of a point mass.
Techniques for clustering interaction data as a collection of graphs
Authors: Nam H. Lee, Carey Priebe, Youngser Park, I-Jeng Wang, Michael Rosen
Date: 2014-06-24 00:00:00
Tasks: Clustering, Community Detection, Model Selection, Time Series, Time Series Analysis

A natural approach to analyze interaction data of form
"what-connects-to-what-when" is to create a time-series (or rather a sequence)
of graphs through temporal discretization (bandwidth selection) and spatial
discretization (vertex contraction). Such discretization together with
non-negative factorization techniques can be useful for obtaining clustering of
graphs. Motivating application of performing clustering of graphs (as opposed
to vertex clustering) can be found in neuroscience and in social network
analysis, and it can also be used to enhance community detection (i.e., vertex
clustering) by way of conditioning on the cluster labels. In this paper, we
formulate a problem of clustering of graphs as a model selection problem. Our
approach involves information criteria, non-negative matrix factorization and
singular value thresholding, and we illustrate our techniques using real and
simulated data.
Riemannian Metric Learning for Symmetric Positive Definite Matrices
Authors: Raviteja Vemulapalli, David W. Jacobs
Date: 2015-01-10 00:00:00
Tasks: Clustering, Metric Learning

Over the past few years, symmetric positive definite (SPD) matrices have been
receiving considerable attention from computer vision community. Though various
distance measures have been proposed in the past for comparing SPD matrices,
the two most widely-used measures are affine-invariant distance and
log-Euclidean distance. This is because these two measures are true geodesic
distances induced by Riemannian geometry. In this work, we focus on the
log-Euclidean Riemannian geometry and propose a data-driven approach for
learning Riemannian metrics/geodesic distances for SPD matrices. We show that
the geodesic distance learned using the proposed approach performs better than
various existing distance measures when evaluated on face matching and
clustering tasks.
Autonomous Farm Vehicles: Prototype of Power Reaper
Authors: Abdul Qadeer Khan, Ayyaz Akhtar, Muhammad Zubair Ahmad
Date: 2015-01-10 00:00:00

Chapter 2 will begin with introduction of Agricultural Robotics. There will
be a literature review of the mechanical structure, vision and control
algorithms. In chapter 3 we will discuss the methodology in detail using block
diagrams and flowcharts. The results of the tested and the proposed algorithms
will also be displayed. In chapter 4 we will discuss the results in detail and
how they are of significance in our work. In chapter 5 we will conclude our
work and discuss some future perspectives. In appendices we will provide some
background information necessary regarding this project.
Low Cost Semi-Autonomous Agricultural Robots In Pakistan-Vision Based Navigation Scalable methodology for wheat harvesting
Authors: Muhammad Zubair Ahmad, Ayyaz Akhtar, Abdul Qadeer Khan, Amir Ali Khan, Muhammad Murtaza Khan
Date: 2015-01-10 00:00:00

Robots have revolutionized our way of life in recent years.One of the domains
that has not yet completely benefited from the robotic automation is the
agricultural sector. Agricultural Robotics should complement humans in the
arduous tasks during different sub-domains of this sector. Extensive research
in Agricultural Robotics has been carried out in Japan, USA, Australia and
Germany focusing mainly on the heavy agricultural machinery. Pakistan is an
agricultural rich country and its economy and food security are closely tied
with agriculture in general and wheat in particular. However, agricultural
research in Pakistan is still carried out using the conventional methodologies.
This paper is an attempt to trigger the research in this modern domain so that
we can benefit from cost effective and resource efficient autonomous
agricultural methodologies. This paper focuses on a scalable low cost
semi-autonomous technique for wheat harvest which primarily focuses on the
farmers with small land holdings. The main focus will be on the vision part of
the navigation system deployed by the proposed robot.
Simplified vision based automatic navigation for wheat harvesting in low income economies
Authors: Muhammad Zubair Ahmad, Ayyaz Akhtar, Abdul Qadeer Khan, Amir A. Khan
Date: 2015-01-10 00:00:00

Recent developments in the domain of agricultural robotics have resulted in
development of complex and efficient systems. Most of the land owners in the
South Asian region are low income farmers. The agricultural experience for them
is still a completely manual process. However, the extreme weather conditions,
heat and flooding, often combine to put a lot of stress on these small land
owners and the associated labor. In this paper, we propose a prototype for an
automated power reaper for the wheat crop. This automated vehicle is navigated
using a simple vision based approach employing the low-cost camera and assisted
GPS. The mechanical platform is driven by three motors controlled through an
interface between the proposed vision algorithm and the electrical drive. The
proposed methodology is applied on some real field scenarios to demonstrate the
efficiency of the vision based algorithm.
On the Distribution of Salient Objects in Web Images and its Influence on Salient Object Detection
Authors: Boris Schauerte, Rainer Stiefelhagen
Date: 2015-01-10 00:00:00
Tasks: Object, object-detection, Object Detection, RGB Salient Object Detection, Saliency Detection, Salient Object Detection

It has become apparent that a Gaussian center bias can serve as an important
prior for visual saliency detection, which has been demonstrated for predicting
human eye fixations and salient object detection. Tseng et al. have shown that
the photographer's tendency to place interesting objects in the center is a
likely cause for the center bias of eye fixations. We investigate the influence
of the photographer's center bias on salient object detection, extending our
previous work. We show that the centroid locations of salient objects in
photographs of Achanta and Liu's data set in fact correlate strongly with a
Gaussian model. This is an important insight, because it provides an empirical
motivation and justification for the integration of such a center bias in
salient object detection algorithms and helps to understand why Gaussian models
are so effective. To assess the influence of the center bias on salient object
detection, we integrate an explicit Gaussian center bias model into two
state-of-the-art salient object detection algorithms. This way, first, we
quantify the influence of the Gaussian center bias on pixel- and segment-based
salient object detection. Second, we improve the performance in terms of F1
score, Fb score, area under the recall-precision curve, area under the receiver
operating characteristic curve, and hit-rate on the well-known data set by
Achanta and Liu. Third, by debiasing Cheng et al.'s region contrast model, we
exemplarily demonstrate that implicit center biases are partially responsible
for the outstanding performance of state-of-the-art algorithms. Last but not
least, as a result of debiasing Cheng et al.'s algorithm, we introduce a
non-biased salient object detection method, which is of interest for
applications in which the image data is not likely to have a photographer's
center bias (e.g., image data of surveillance cameras or autonomous robots).
Graph Matching: Relax at Your Own Risk
Authors: Vince Lyzinski, Donniell Fishkind, Marcelo Fiori, Joshua T. Vogelstein, Carey E. Priebe, Guillermo Sapiro
Date: 2014-05-13 00:00:00
Tasks: Graph Matching

Graph matching---aligning a pair of graphs to minimize their edge
disagreements---has received wide-spread attention from both theoretical and
applied communities over the past several decades, including combinatorics,
computer vision, and connectomics. Its attention can be partially attributed to
its computational difficulty. Although many heuristics have previously been
proposed in the literature to approximately solve graph matching, very few have
any theoretical support for their performance. A common technique is to relax
the discrete problem to a continuous problem, therefore enabling practitioners
to bring gradient-descent-type algorithms to bear. We prove that an indefinite
relaxation (when solved exactly) almost always discovers the optimal
permutation, while a common convex relaxation almost always fails to discover
the optimal permutation. These theoretical results suggest that initializing
the indefinite algorithm with the convex optimum might yield improved practical
performance. Indeed, experimental results illuminate and corroborate these
theoretical findings, demonstrating that excellent results are achieved in both
benchmark and real data problems by amalgamating the two approaches.
Exploring Sparsity in Multi-class Linear Discriminant Analysis
Authors: Dong Xia
Date: 2014-12-26 00:00:00
Tasks: Binary Classification, Classification, General Classification

Recent studies in the literature have paid much attention to the sparsity in
linear classification tasks. One motivation of imposing sparsity assumption on
the linear discriminant direction is to rule out the noninformative features,
making hardly contribution to the classification problem. Most of those work
were focused on the scenarios of binary classification. In the presence of
multi-class data, preceding researches recommended individually pairwise sparse
linear discriminant analysis(LDA). However, further sparsity should be
explored. In this paper, an estimator of grouped LASSO type is proposed to take
advantage of sparsity for multi-class data. It enjoys appealing non-asymptotic
properties which allows insignificant correlations among features. This
estimator exhibits superior capability on both simulated and real data.
Survey schemes for stochastic gradient descent with applications to M-estimation
Authors: Stéphan Clémençon, Patrice Bertail, Emilie Chautru, Guillaume Papa
Date: 2015-01-09 00:00:00
Tasks: Survey, Survey Sampling

In certain situations that shall be undoubtedly more and more common in the
Big Data era, the datasets available are so massive that computing statistics
over the full sample is hardly feasible, if not unfeasible. A natural approach
in this context consists in using survey schemes and substituting the "full
data" statistics with their counterparts based on the resulting random samples,
of manageable size. It is the main purpose of this paper to investigate the
impact of survey sampling with unequal inclusion probabilities on stochastic
gradient descent-based M-estimation methods in large-scale statistical and
machine-learning problems. Precisely, we prove that, in presence of some a
priori information, one may significantly increase asymptotic accuracy when
choosing appropriate first order inclusion probabilities, without affecting
complexity. These striking results are described here by limit theorems and are
also illustrated by numerical experiments.
Investigation of a chaotic spiking neuron model
Authors: M. Alhawarat, T. Olde Scheper, N. T. Crook
Date: 2015-01-09 00:00:00
Tasks: model

Chaos provides many interesting properties that can be used to achieve
computational tasks. Such properties are sensitivity to initial conditions,
space filling, control and synchronization. Chaotic neural models have been
devised to exploit such properties. In this paper, a chaotic spiking neuron
model is investigated experimentally. This investigation is performed to
understand the dynamic behaviours of the model.
  The aim of this research is to investigate the dynamics of the nonlinear
dynamic state neuron (NDS) experimentally. The experimental approach has
revealed some quantitative and qualitative properties of the NDS model such as
the control mechanism, the reset mechanism, and the way the model may exhibit
dynamic behaviours in phase space. It is shown experimentally in this paper
that both the reset mechanism and the self-feed back control mechanism are
important for the NDS model to work and to stabilise to one of the large number
of available unstable periodic orbits (UPOs) that are embedded in its
attractor. The experimental investigation suggests that the internal dynamics
of the NDS neuron provide a rich set of dynamic behaviours that can be
controlled and stabilised. These wide range of dynamic behaviours may be
exploited to carry out information processing tasks.
Introduction and Ranking Results of the ICSI 2014 Competition on Single Objective Optimization
Authors: Ying Tan, Junzhi Li, Zhongyang Zheng
Date: 2015-01-09 00:00:00

This technical report includes the introduction and ranking results of the
ICSI 2014 Competition on Single Objective Optimization.
Filter Design and Performance Evaluation for Fingerprint Image Segmentation
Authors: Duy Hoang Thai, Stephan Huckemann, Carsten Gottschlich
Date: 2015-01-09 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Fingerprint recognition plays an important role in many commercial
applications and is used by millions of people every day, e.g. for unlocking
mobile phones. Fingerprint image segmentation is typically the first processing
step of most fingerprint algorithms and it divides an image into foreground,
the region of interest, and background. Two types of error can occur during
this step which both have a negative impact on the recognition performance:
'true' foreground can be labeled as background and features like minutiae can
be lost, or conversely 'true' background can be misclassified as foreground and
spurious features can be introduced. The contribution of this paper is
threefold: firstly, we propose a novel factorized directional bandpass (FDB)
segmentation method for texture extraction based on the directional Hilbert
transform of a Butterworth bandpass (DHBB) filter interwoven with
soft-thresholding. Secondly, we provide a manually marked ground truth
segmentation for 10560 images as an evaluation benchmark. Thirdly, we conduct a
systematic performance comparison between the FDB method and four of the most
often cited fingerprint segmentation algorithms showing that the FDB
segmentation method clearly outperforms these four widely used methods. The
benchmark and the implementation of the FDB method are made publicly available.
HOG based Fast Human Detection
Authors: M. Kachouane, S. Sahki, M. Lakrouf, N. Ouadah
Date: 2015-01-09 00:00:00
Tasks: Human Detection

Objects recognition in image is one of the most difficult problems in
computer vision. It is also an important step for the implementation of several
existing applications that require high-level image interpretation. Therefore,
there is a growing interest in this research area during the last years. In
this paper, we present an algorithm for human detection and recognition in
real-time, from images taken by a CCD camera mounted on a car-like mobile
robot. The proposed technique is based on Histograms of Oriented Gradient (HOG)
and SVM classifier. The implementation of our detector has provided good
results, and can be used in robotics tasks.
Improving the Deductive System DES with Persistence by Using SQL DBMS's
Authors: Fernando Sáenz-Pérez
Date: 2015-01-09 00:00:00
Tasks: Management

This work presents how persistent predicates have been included in the
in-memory deductive system DES by relying on external SQL database management
systems. We introduce how persistence is supported from a user-point of view
and the possible applications the system opens up, as the deductive expressive
power is projected to relational databases. Also, we describe how it is
possible to intermix computations of the deductive engine and the external
database, explaining its implementation and some optimizations. Finally, a
performance analysis is undertaken, comparing the system with current
relational database systems.
Existential Rule Languages with Finite Chase: Complexity and Expressiveness
Authors: Heng Zhang, Yan Zhang, Jia-Huai You
Date: 2014-11-19 00:00:00

Finite chase, or alternatively chase termination, is an important condition
to ensure the decidability of existential rule languages. In the past few
years, a number of rule languages with finite chase have been studied. In this
work, we propose a novel approach for classifying the rule languages with
finite chase. Using this approach, a family of decidable rule languages, which
extend the existing languages with the finite chase property, are naturally
defined. We then study the complexity of these languages. Although all of them
are tractable for data complexity, we show that their combined complexity can
be arbitrarily high. Furthermore, we prove that all the rule languages with
finite chase that extend the weakly acyclic language are of the same
expressiveness as the weakly acyclic one, while rule languages with higher
combined complexity are in general more succinct than those with lower combined
complexity.
Co-clustering for directed graphs: the Stochastic co-Blockmodel and spectral algorithm Di-Sim
Authors: Karl Rohe, Tai Qin, Bin Yu
Date: 2012-04-10 00:00:00
Tasks: Clustering, Graph Clustering

Directed graphs have asymmetric connections, yet the current graph clustering
methodologies cannot identify the potentially global structure of these
asymmetries. We give a spectral algorithm called di-sim that builds on a dual
measure of similarity that correspond to how a node (i) sends and (ii) receives
edges. Using di-sim, we analyze the global asymmetries in the networks of Enron
emails, political blogs, and the c elegans neural connectome. In each example,
a small subset of nodes have persistent asymmetries; these nodes send edges
with one cluster, but receive edges with another cluster. Previous approaches
would have assigned these asymmetric nodes to only one cluster, failing to
identify their sending/receiving asymmetries. Regularization and "projection"
are two steps of di-sim that are essential for spectral clustering algorithms
to work in practice. The theoretical results show that these steps make the
algorithm weakly consistent under the degree corrected Stochastic
co-Blockmodel, a model that generalizes the Stochastic Blockmodel to allow for
both (i) degree heterogeneity and (ii) the global asymmetries that we intend to
detect. The theoretical results make no assumptions on the smallest degree
nodes. Instead, the theorem requires that the average degree grows sufficiently
fast and that the weak consistency only applies to the subset of the nodes with
sufficiently large leverage scores. The results results also apply to bipartite
graphs.