pwc-archive/papers-with-abstracts huggingface dataset:
On Classification with Bags, Groups and Sets
Authors: Veronika Cheplygina, David M. J. Tax, Marco Loog
Date: 2014-06-02 00:00:00
Tasks: Classification, General Classification

Many classification problems can be difficult to formulate directly in terms
of the traditional supervised setting, where both training and test samples are
individual feature vectors. There are cases in which samples are better
described by sets of feature vectors, that labels are only available for sets
rather than individual samples, or, if individual labels are available, that
these are not independent. To better deal with such problems, several
extensions of supervised learning have been proposed, where either training
and/or test objects are sets of feature vectors. However, having been proposed
rather independently of each other, their mutual similarities and differences
have hitherto not been mapped out. In this work, we provide an overview of such
learning scenarios, propose a taxonomy to illustrate the relationships between
them, and discuss directions for further research in these areas.
Mumford-Shah and Potts Regularization for Manifold-Valued Data with Applications to DTI and Q-Ball Imaging
Authors: Andreas Weinmann, Laurent Demaret, Martin Storath
Date: 2014-10-07 00:00:00
Tasks: Denoising

Mumford-Shah and Potts functionals are powerful variational models for
regularization which are widely used in signal and image processing; typical
applications are edge-preserving denoising and segmentation. Being both
non-smooth and non-convex, they are computationally challenging even for scalar
data. For manifold-valued data, the problem becomes even more involved since
typical features of vector spaces are not available. In this paper, we propose
algorithms for Mumford-Shah and for Potts regularization of manifold-valued
signals and images. For the univariate problems, we derive solvers based on
dynamic programming combined with (convex) optimization techniques for
manifold-valued data. For the class of Cartan-Hadamard manifolds (which
includes the data space in diffusion tensor imaging), we show that our
algorithms compute global minimizers for any starting point. For the
multivariate Mumford-Shah and Potts problems (for image regularization) we
propose a splitting into suitable subproblems which we can solve exactly using
the techniques developed for the corresponding univariate problems. Our method
does not require any a priori restrictions on the edge set and we do not have
to discretize the data space. We apply our method to diffusion tensor imaging
(DTI) as well as Q-ball imaging. Using the DTI model, we obtain a segmentation
of the corpus callosum.
Annotation as a New Paradigm in Research Archiving
Authors: Dirk Roorda, Charles van den Heuvel
Date: 2014-10-07 00:00:00
Tasks: Management

We outline a paradigm to preserve results of digital scholarship, whether
they are query results, feature values, or topic assignments. This paradigm is
characterized by using annotations as multifunctional carriers and making them
portable. The testing grounds we have chosen are two significant enterprises,
one in the history of science, and one in Hebrew scholarship. The first one
(CKCC) focuses on the results of a project where a Dutch consortium of
universities, research institutes, and cultural heritage institutions
experimented for 4 years with language techniques and topic modeling methods
with the aim to analyze the emergence of scholarly debates. The data: a complex
set of about 20.000 letters. The second one (DTHB) is a multi-year effort to
express the linguistic features of the Hebrew bible in a text database, which
is still growing in detail and sophistication. Versions of this database are
packaged in commercial bible study software. We state that the results of these
forms of scholarship require new knowledge management and archive practices.
Only when researchers can build efficiently on each other's (intermediate)
results, they can achieve the aggregations of quality data by which new
questions can be answered, and hidden patterns visualized. Archives are
required to find a balance between preserving authoritative versions of sources
and supporting collaborative efforts in digital scholarship. Annotations are
promising vehicles for preserving and reusing research results. Keywords
annotation, portability, archiving, queries, features, topics, keywords,
Republic of Letters, Hebrew text databases.
Predicting Nearly As Well As the Optimal Twice Differentiable Regressor
Authors: N. Denizcan Vanli, Muhammed O. Sayin, Suleyman S. Kozat
Date: 2014-01-23 00:00:00
Tasks: regression

We study nonlinear regression of real valued data in an individual sequence
manner, where we provide results that are guaranteed to hold without any
statistical assumptions. We address the convergence and undertraining issues of
conventional nonlinear regression methods and introduce an algorithm that
elegantly mitigates these issues via an incremental hierarchical structure,
(i.e., via an incremental decision tree). Particularly, we present a piecewise
linear (or nonlinear) regression algorithm that partitions the regressor space
in a data driven manner and learns a linear model at each region. Unlike the
conventional approaches, our algorithm gradually increases the number of
disjoint partitions on the regressor space in a sequential manner according to
the observed data. Through this data driven approach, our algorithm
sequentially and asymptotically achieves the performance of the optimal twice
differentiable regression function for any data sequence with an unknown and
arbitrary length. The computational complexity of the introduced algorithm is
only logarithmic in the data length under certain regularity conditions. We
provide the explicit description of the algorithm and demonstrate the
significant gains for the well-known benchmark real data sets and chaotic
signals.
Term-Weighting Learning via Genetic Programming for Text Classification
Authors: Hugo Jair Escalante, Mauricio A. García-Limón, Alicia Morales-Reyes, Mario Graff, Manuel Montes-y-Gómez, Eduardo F. Morales
Date: 2014-10-02 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, text-classification, Text Classification

This paper describes a novel approach to learning term-weighting schemes
(TWSs) in the context of text classification. In text mining a TWS determines
the way in which documents will be represented in a vector space model, before
applying a classifier. Whereas acceptable performance has been obtained with
standard TWSs (e.g., Boolean and term-frequency schemes), the definition of
TWSs has been traditionally an art. Further, it is still a difficult task to
determine what is the best TWS for a particular problem and it is not clear
yet, whether better schemes, than those currently available, can be generated
by combining known TWS. We propose in this article a genetic program that aims
at learning effective TWSs that can improve the performance of current schemes
in text classification. The genetic program learns how to combine a set of
basic units to give rise to discriminative TWSs. We report an extensive
experimental study comprising data sets from thematic and non-thematic text
classification as well as from image classification. Our study shows the
validity of the proposed method; in fact, we show that TWSs learned with the
genetic program outperform traditional schemes and other TWSs proposed in
recent works. Further, we show that TWSs learned from a specific domain can be
effectively used for other tasks.
Top Rank Optimization in Linear Time
Authors: Nan Li, Rong Jin, Zhi-Hua Zhou
Date: 2014-10-06 00:00:00
Proceeding: NeurIPS 2014 12

Bipartite ranking aims to learn a real-valued ranking function that orders
positive instances before negative instances. Recent efforts of bipartite
ranking are focused on optimizing ranking accuracy at the top of the ranked
list. Most existing approaches are either to optimize task specific metrics or
to extend the ranking loss by emphasizing more on the error associated with the
top ranked instances, leading to a high computational cost that is super-linear
in the number of training instances. We propose a highly efficient approach,
titled TopPush, for optimizing accuracy at the top that has computational
complexity linear in the number of training instances. We present a novel
analysis that bounds the generalization error for the top ranked instances for
the proposed approach. Empirical study shows that the proposed approach is
highly competitive to the state-of-the-art approaches and is 10-100 times
faster.
An Aerial Image Recognition Framework using Discrimination and Redundancy Quality Measure
Authors: Yuxin Hu, Luming Zhang
Date: 2014-10-06 00:00:00
Tasks: Image Categorization

Aerial image categorization plays an indispensable role in remote sensing and
artificial intelligence. In this paper, we propose a new aerial image
categorization framework, focusing on organizing the local patches of each
aerial image into multiple discriminative subgraphs. The subgraphs reflect both
the geometric property and the color distribution of an aerial image. First,
each aerial image is decomposed into a collection of regions in terms of their
color intensities. Thereby region connected graph (RCG), which models the
connection between the spatial neighboring regions, is constructed to encode
the spatial context of an aerial image. Second, a subgraph mining technique is
adopted to discover the frequent structures in the RCGs constructed from the
training aerial images. Thereafter, a set of refined structures are selected
among the frequent ones toward being highly discriminative and low redundant.
Lastly, given a new aerial image, its sub-RCGs corresponding to the refined
structures are extracted. They are further quantized into a discriminative
vector for SVM classification. Thorough experimental results validate the
effectiveness of the proposed method. In addition, the visualized mined
subgraphs show that the discriminative topologies of each aerial image are
discovered.
Effective persistent homology of digital images
Authors: Ana Romero, Julio Rubio, Francis Sergeraert
Date: 2014-10-06 00:00:00

In this paper, three Computational Topology methods (namely effective
homology, persistent homology and discrete vector fields) are mixed together to
produce algorithms for homological digital image processing. The algorithms
have been implemented as extensions of the Kenzo system and have shown a good
performance when applied on some actual images extracted from a public dataset.
Memristive Threshold Logic Circuit Design of Fast Moving Object Detection
Authors: Akshay Kumar Maan, Dinesh Sasi Kumar, Sherin Sugathan, Alex Pappachen James
Date: 2014-10-06 00:00:00
Tasks: Moving Object Detection, object-detection, Object Detection

Real-time detection of moving objects involves memorisation of features in
the template image and their comparison with those in the test image. At high
sampling rates, such techniques face the problems of high algorithmic
complexity and component delays. We present a new resistive switching based
threshold logic cell which encodes the pixels of a template image. The cell
comprises a voltage divider circuit that programs the resistances of the
memristors arranged in a single node threshold logic network and the output is
encoded as a binary value using a CMOS inverter gate. When a test image is
applied to the template-programmed cell, a mismatch in the respective pixels is
seen as a change in the output voltage of the cell. The proposed cell when
compared with CMOS equivalent implementation shows improved performance in
area, leakage power, power dissipation and delay.
Sequential Monte Carlo for Graphical Models
Authors: Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön
Date: 2014-02-03 00:00:00
Proceeding: NeurIPS 2014 12

We propose a new framework for how to use sequential Monte Carlo (SMC)
algorithms for inference in probabilistic graphical models (PGM). Via a
sequential decomposition of the PGM we find a sequence of auxiliary
distributions defined on a monotonically increasing sequence of probability
spaces. By targeting these auxiliary distributions using SMC we are able to
approximate the full joint distribution defined by the PGM. One of the key
merits of the SMC sampler is that it provides an unbiased estimate of the
partition function of the model. We also show how it can be used within a
particle Markov chain Monte Carlo framework in order to construct
high-dimensional block-sampling algorithms for general PGMs.
Algebraic Semantics of Proto-Transitive Rough Sets
Authors: A. Mani
Date: 2014-10-02 00:00:00

Rough sets over generalized transitive relations like proto-transitive ones
had been initiated by the present author in the year 2012. Subsequently,
approximation of proto-transitive relations by other relations was investigated
and the relation with rough approximations was developed towards constructing
semantics that can handle fragments of structure. It was also proved that
difference of approximations induced by some approximate relations need not
induce rough structures. In this research we develop different semantics of
proto transitive rough sets (PRAX) after characterizing the structure of rough
objects and also develop a theory of dependence for general rough sets and use
it to internalize the Nelson-algebra based approximate semantics developed
earlier. The theory of rough dependence initiated later by the present author
is extended in the process. This monograph is reasonably self-contained and
includes proofs and extensions of representation of objects that were not part
of earlier papers.
Coupling Top-down and Bottom-up Methods for 3D Human Pose and Shape Estimation from Monocular Image Sequences
Authors: Atul Kanaujia
Date: 2014-10-01 00:00:00
Tasks: 3D human pose and shape estimation

Until recently Intelligence, Surveillance, and Reconnaissance (ISR) focused
on acquiring behavioral information of the targets and their activities.
Continuous evolution of intelligence being gathered of the human centric
activities has put increased focus on the humans, especially inferring their
innate characteristics - size, shapes and physiology. These bio-signatures
extracted from the surveillance sensors can be used to deduce age, ethnicity,
gender and actions, and further characterize human actions in unseen scenarios.
However, recovery of pose and shape of humans in such monocular videos is
inherently an ill-posed problem, marked by frequent depth and view based
ambiguities due to self-occlusion, foreshortening and misalignment. The
likelihood function often yields a highly multimodal posterior that is
difficult to propagate even using the most advanced particle filtering(PF)
algorithms. Motivated by the recent success of the discriminative approaches to
efficiently predict 3D poses directly from the 2D images, we present several
principled approaches to integrate predictive cues using learned regression
models to sustain multimodality of the posterior during tracking. Additionally,
these learned priors can be actively adapted to the test data using a
likelihood based feedback mechanism. Estimated 3D poses are then used to fit 3D
human shape model to each frame independently for inferring anthropometric
bio-signatures. The proposed system is fully automated, robust to noisy test
data and has ability to swiftly recover from tracking failures even after
confronting with significant errors. We evaluate the system on a large number
of monocular human motion sequences.
Bayesian regression and Bitcoin
Authors: Devavrat Shah, Kang Zhang
Date: 2014-10-06 00:00:00
Tasks: Bayesian Inference, Binary Classification, General Classification, regression

In this paper, we discuss the method of Bayesian regression and its efficacy
for predicting price variation of Bitcoin, a recently popularized virtual,
cryptographic currency. Bayesian regression refers to utilizing empirical data
as proxy to perform Bayesian inference. We utilize Bayesian regression for the
so-called "latent source model". The Bayesian regression for "latent source
model" was introduced and discussed by Chen, Nikolov and Shah (2013) and
Bresler, Chen and Shah (2014) for the purpose of binary classification. They
established theoretical as well as empirical efficacy of the method for the
setting of binary classification.
  In this paper, instead we utilize it for predicting real-valued quantity, the
price of Bitcoin. Based on this price prediction method, we devise a simple
strategy for trading Bitcoin. The strategy is able to nearly double the
investment in less than 60 day period when run against real data trace.
Learning Topology and Dynamics of Large Recurrent Neural Networks
Authors: Yiyuan She, Yuejia He, Dapeng Wu
Date: 2014-10-05 00:00:00

Large-scale recurrent networks have drawn increasing attention recently
because of their capabilities in modeling a large variety of real-world
phenomena and physical mechanisms. This paper studies how to identify all
authentic connections and estimate system parameters of a recurrent network,
given a sequence of node observations. This task becomes extremely challenging
in modern network applications, because the available observations are usually
very noisy and limited, and the associated dynamical system is strongly
nonlinear. By formulating the problem as multivariate sparse sigmoidal
regression, we develop simple-to-implement network learning algorithms, with
rigorous convergence guarantee in theory, for a variety of sparsity-promoting
penalty forms. A quantile variant of progressive recurrent network screening is
proposed for efficient computation and allows for direct cardinality control of
network topology in estimation. Moreover, we investigate recurrent network
stability conditions in Lyapunov's sense, and integrate such stability
constraints into sparse network learning. Experiments show excellent
performance of the proposed algorithms in network topology identification and
forecasting.
1-HKUST: Object Detection in ILSVRC 2014
Authors: Cewu Lu, Hao Chen, Qifeng Chen, Hei Law, Yao Xiao, Chi-Keung Tang
Date: 2014-09-22 00:00:00
Tasks: Object, object-detection, Object Detection, Object Localization, Object Recognition

The Imagenet Large Scale Visual Recognition Challenge (ILSVRC) is the one of
the most important big data challenges to date. We participated in the object
detection track of ILSVRC 2014 and received the fourth place among the 38
teams. We introduce in our object detection system a number of novel techniques
in localization and recognition. For localization, initial candidate proposals
are generated using selective search, and a novel bounding boxes regression
method is used for better object localization. For recognition, to represent a
candidate proposal, we adopt three features, namely, RCNN feature, IFV feature,
and DPM feature. Given these features, category-specific combination functions
are learned to improve the object recognition rate. In addition, object context
in the form of background priors and object interaction priors are learned and
applied in our system. Our ILSVRC 2014 results are reported alongside with the
results of other participating teams.
Training Algorithm for Neuro-Fuzzy Network Based on Singular Spectrum Analysis
Authors: Yulia S. Maslennikova, Vladimir V. Bochkarev
Date: 2014-10-05 00:00:00
Tasks: Time Series, Time Series Analysis

In this article, we propose a combination of an noise-reduction algorithm
based on Singular Spectrum Analysis (SSA) and a standard feedforward neural
prediction model. Basically, the proposed algorithm consists of two different
steps: data preprocessing based on the SSA filtering method and step-by-step
training procedure in which we use a simple feedforward multilayer neural
network with backpropagation learning. The proposed noise-reduction procedure
successfully removes most of the noise. That increases long-term predictability
of the processed dataset comparison with the raw dataset. The method was
applied to predict the International sunspot number RZ time series. The results
show that our combined technique has better performances than those offered by
the same network directly applied to raw dataset.
Corpora Preparation and Stopword List Generation for Arabic data in Social Network
Authors: Walaa Medhat, Ahmed H. Yousef, Hoda Korashy
Date: 2014-10-05 00:00:00
Tasks: feature selection, General Classification, Sentiment Analysis, text-classification, Text Classification

This paper proposes a methodology to prepare corpora in Arabic language from
online social network (OSN) and review site for Sentiment Analysis (SA) task.
The paper also proposes a methodology for generating a stopword list from the
prepared corpora. The aim of the paper is to investigate the effect of removing
stopwords on the SA task. The problem is that the stopwords lists generated
before were on Modern Standard Arabic (MSA) which is not the common language
used in OSN. We have generated a stopword list of Egyptian dialect and a
corpus-based list to be used with the OSN corpora. We compare the efficiency of
text classification when using the generated lists along with previously
generated lists of MSA and combining the Egyptian dialect list with the MSA
list. The text classification was performed using Na\"ive Bayes and Decision
Tree classifiers and two feature selection approaches, unigrams and bigram. The
experiments show that the general lists containing the Egyptian dialects words
give better performance than using lists of MSA stopwords only.
Statistique et Big Data Analytics; Volumétrie, L'Attaque des Clones
Authors: Philippe Besse, Nathalie Villa-Vialaneix
Date: 2014-05-26 00:00:00
Tasks: regression

This article assumes acquired the skills and expertise of a statistician in
unsupervised (NMF, k-means, SVD) and supervised learning (regression, CART,
random forest). What skills and knowledge do a statistician must acquire to
reach the "Volume" scale of big data? After a quick overview of the different
strategies available and especially of those imposed by Hadoop, the algorithms
of some available learning methods are outlined in order to understand how they
are adapted to the strong stresses of the Map-Reduce functionalities