pwc-archive/papers-with-abstracts huggingface dataset:
On the Computational Intractability of Exact and Approximate Dictionary Learning
Authors: Andreas M. Tillmann
Date: 2014-05-26 00:00:00
Tasks: compressed sensing, Dictionary Learning

The efficient sparse coding and reconstruction of signal vectors via linear
observations has received a tremendous amount of attention over the last
decade. In this context, the automated learning of a suitable basis or
overcomplete dictionary from training data sets of certain signal classes for
use in sparse representations has turned out to be of particular importance
regarding practical signal processing applications. Most popular dictionary
learning algorithms involve NP-hard sparse recovery problems in each iteration,
which may give some indication about the complexity of dictionary learning but
does not constitute an actual proof of computational intractability. In this
technical note, we show that learning a dictionary with which a given set of
training signals can be represented as sparsely as possible is indeed NP-hard.
Moreover, we also establish hardness of approximating the solution to within
large factors of the optimal sparsity level. Furthermore, we give NP-hardness
and non-approximability results for a recent dictionary learning variation
called the sensor permutation problem. Along the way, we also obtain a new
non-approximability result for the classical sparse recovery problem from
compressed sensing.
Adaptive Wavelet Based Identification and Extraction of PQRST Combination in Randomly Stretching ECG Sequence
Authors: T. R. Gopalakrishnan Nair, A. P. Geetha, M. Asharani
Date: 2014-08-03 00:00:00
Tasks: TAG

Cardiovascular system study using ECG signals have evolved tremendously in
the domain of electronics and signal processing. However, there are certain
floating challenges unresolved in the analysis and detection of abnormal
performances of cardiovascular system. As the medical field is moving towards
more automated and intelligent systems, wrong detection or wrong
interpretations of ECG waveform of abnormal conditions can be quite fatal.
Since the PQRST signals vary their positions randomly, the process of locating,
identifying and classifying each feature can be cumbersome and it is prone to
errors. Here we present an automated scheme using adaptive wavelet to detect
prominent R-peak with extreme accuracy and algorithmically tag and mark the
coexisting peaks P, Q, S, and T with almost same accuracy. The adaptive wavelet
approach used in this scheme is capable of detecting R-peak in ECG with 99.99%
accuracy along with the rest of the waveforms.
Methodology For Detection of QRS Pattern Using Secondary Wavelets
Authors: T. R. Gopalakrishnan Nair, A. P. Geetha, Asharani
Date: 2014-08-03 00:00:00
Tasks: Position

Applications of wavelet transform to the field of health care signals have
paved the way for implementing revolutionary approaches in detecting the
presence of certain abnormalities in human health patterns. There were
extensive studies carried out using primary wavelets in various signals like
Electrocardiogram (ECG), sonogram etc. with a certain amount of success. On the
other hand analysis using secondary wavelets which inherits the characteristics
of a set of variations available in signals like ECG can be a promise to detect
diseases with ease. Here a method to create a generalized adapted wavelet is
presented which contains the information of QRS pattern collected from an
anomaly sample space. The method has been tested and found to be successful in
locating the position of R peak in noise embedded ECG signal.
A Bayesian estimation approach to analyze non-Gaussian data-generating processes with latent classes
Authors: Naoki Tanaka, Shohei Shimizu, Takashi Washio
Date: 2014-08-02 00:00:00

A large amount of observational data has been accumulated in various fields
in recent times, and there is a growing need to estimate the generating
processes of these data. A linear non-Gaussian acyclic model (LiNGAM) based on
the non-Gaussianity of external influences has been proposed to estimate the
data-generating processes of variables. However, the results of the estimation
can be biased if there are latent classes. In this paper, we first review
LiNGAM, its extended model, as well as the estimation procedure for LiNGAM in a
Bayesian framework. We then propose a new Bayesian estimation procedure that
solves the problem.
Weakly monotone averaging functions
Authors: Tim Wilkin, Gleb Beliakov
Date: 2014-08-02 00:00:00

Monotonicity with respect to all arguments is fundamental to the definition
of aggregation functions. It is also a limiting property that results in many
important non-monotonic averaging functions being excluded from the theoretical
framework. This work proposes a definition for weakly monotonic averaging
functions, studies some properties of this class of functions and proves that
several families of important non-monotonic means are actually weakly monotonic
averaging functions. Specifically we provide sufficient conditions for weak
monotonicity of the Lehmer mean and generalised mixture operators. We establish
weak monotonicity of several robust estimators of location and conditions for
weak monotonicity of a large class of penalty-based aggregation functions.
These results permit a proof of the weak monotonicity of the class of
spatial-tonal filters that include important members such as the bilateral
filter and anisotropic diffusion. Our concept of weak monotonicity provides a
sound theoretical and practical basis by which (monotone) aggregation functions
and non-monotone averaging functions can be related within the same framework,
allowing us to bridge the gap between these previously disparate areas of
research.
Matrix Factorization with Explicit Trust and Distrust Relationships
Authors: Rana Forsati, Mehrdad Mahdavi, Mehrnoush Shamsfard, Mohamed Sarwat
Date: 2014-08-02 00:00:00
Tasks: Recommendation Systems

With the advent of online social networks, recommender systems have became
crucial for the success of many online applications/services due to their
significance role in tailoring these applications to user-specific needs or
preferences. Despite their increasing popularity, in general recommender
systems suffer from the data sparsity and the cold-start problems. To alleviate
these issues, in recent years there has been an upsurge of interest in
exploiting social information such as trust relations among users along with
the rating data to improve the performance of recommender systems. The main
motivation for exploiting trust information in recommendation process stems
from the observation that the ideas we are exposed to and the choices we make
are significantly influenced by our social context. However, in large user
communities, in addition to trust relations, the distrust relations also exist
between users. For instance, in Epinions the concepts of personal "web of
trust" and personal "block list" allow users to categorize their friends based
on the quality of reviews into trusted and distrusted friends, respectively. In
this paper, we propose a matrix factorization based model for recommendation in
social rating networks that properly incorporates both trust and distrust
relationships aiming to improve the quality of recommendations and mitigate the
data sparsity and the cold-start users issues. Through experiments on the
Epinions data set, we show that our new algorithm outperforms its standard
trust-enhanced or distrust-enhanced counterparts with respect to accuracy,
thereby demonstrating the positive effect that incorporation of explicit
distrust information can have on recommender systems.
Generalized Species Sampling Priors with Latent Beta reinforcements
Authors: Edoardo M. Airoldi, Thiago Costa, Federico Bassetti, Fabrizio Leisen, Michele Guindani
Date: 2010-12-04 00:00:00
Tasks: Clustering

Many popular Bayesian nonparametric priors can be characterized in terms of
exchangeable species sampling sequences. However, in some applications,
exchangeability may not be appropriate. We introduce a {novel and
probabilistically coherent family of non-exchangeable species sampling
sequences characterized by a tractable predictive probability function with
weights driven by a sequence of independent Beta random variables. We compare
their theoretical clustering properties with those of the Dirichlet Process and
the two parameters Poisson-Dirichlet process. The proposed construction
provides a complete characterization of the joint process, differently from
existing work. We then propose the use of such process as prior distribution in
a hierarchical Bayes modeling framework, and we describe a Markov Chain Monte
Carlo sampler for posterior inference. We evaluate the performance of the prior
and the robustness of the resulting inference in a simulation study, providing
a comparison with popular Dirichlet Processes mixtures and Hidden Markov
Models. Finally, we develop an application to the detection of chromosomal
aberrations in breast cancer by leveraging array CGH data.
Functional Principal Component Analysis and Randomized Sparse Clustering Algorithm for Medical Image Analysis
Authors: Nan Lin, Junhai Jiang, Shicheng Guo, Momiao Xiong
Date: 2014-08-01 00:00:00
Tasks: Clustering, Disease Prediction, feature selection, Management, Medical Image Analysis

Due to advances in sensors, growing large and complex medical image data have
the ability to visualize the pathological change in the cellular or even the
molecular level or anatomical changes in tissues and organs. As a consequence,
the medical images have the potential to enhance diagnosis of disease,
prediction of clinical outcomes, characterization of disease progression,
management of health care and development of treatments, but also pose great
methodological and computational challenges for representation and selection of
features in image cluster analysis. To address these challenges, we first
extend one dimensional functional principal component analysis to the two
dimensional functional principle component analyses (2DFPCA) to fully capture
space variation of image signals. Image signals contain a large number of
redundant and irrelevant features which provide no additional or no useful
information for cluster analysis. Widely used methods for removing redundant
and irrelevant features are sparse clustering algorithms using a lasso-type
penalty to select the features. However, the accuracy of clustering using a
lasso-type penalty depends on how to select penalty parameters and a threshold
for selecting features. In practice, they are difficult to determine. Recently,
randomized algorithms have received a great deal of attention in big data
analysis. This paper presents a randomized algorithm for accurate feature
selection in image cluster analysis. The proposed method is applied to ovarian
and kidney cancer histology image data from the TCGA database. The results
demonstrate that the randomized feature selection method coupled with
functional principal component analysis substantially outperforms the current
sparse clustering algorithms in image cluster analysis.
A RobustICA Based Algorithm for Blind Separation of Convolutive Mixtures
Authors: Zaid Albataineh, Fathi M. Salem
Date: 2014-08-01 00:00:00
Tasks: blind source separation

We propose a frequency domain method based on robust independent component
analysis (RICA) to address the multichannel Blind Source Separation (BSS)
problem of convolutive speech mixtures in highly reverberant environments. We
impose regularization processes to tackle the ill-conditioning problem of the
covariance matrix and to mitigate the performance degradation in the frequency
domain. We apply an algorithm to separate the source signals in adverse
conditions, i.e. high reverberation conditions when short observation signals
are available. Furthermore, we study the impact of several parameters on the
performance of separation, e.g. overlapping ratio and window type of the
frequency domain method. We also compare different techniques to solve the
frequency-domain permutation ambiguity. Through simulations and real world
experiments, we verify the superiority of the presented convolutive algorithm
among other BSS algorithms, including recursive regularized ICA (RR ICA),
independent vector analysis (IVA).
OpenML: networked science in machine learning
Authors: Joaquin Vanschoren, Jan N. van Rijn, Bernd Bischl, Luis Torgo
Date: 2014-07-29 00:00:00
Tasks: BIG-bench Machine Learning

Many sciences have made significant breakthroughs by adopting online tools
that help organize, structure and mine information that is too detailed to be
printed in journals. In this paper, we introduce OpenML, a place for machine
learning researchers to share and organize data in fine detail, so that they
can work more effectively, be more visible, and collaborate with others to
tackle harder problems. We discuss how OpenML relates to other examples of
networked science and what benefits it brings for machine learning research,
individual scientists, as well as students and practitioners.
Randomized Memetic Artificial Bee Colony Algorithm
Authors: Sandeep Kumar, Vivek Kumar Sharma, Rajani Kumari
Date: 2014-08-01 00:00:00
Tasks: global-optimization

Artificial Bee Colony (ABC) optimization algorithm is one of the recent
population based probabilistic approach developed for global optimization. ABC
is simple and has been showed significant improvement over other Nature
Inspired Algorithms (NIAs) when tested over some standard benchmark functions
and for some complex real world optimization problems. Memetic Algorithms also
become one of the key methodologies to solve the very large and complex
real-world optimization problems. The solution search equation of Memetic ABC
is based on Golden Section Search and an arbitrary value which tries to balance
exploration and exploitation of search space. But still there are some chances
to skip the exact solution due to its step size. In order to balance between
diversification and intensification capability of the Memetic ABC, it is
randomized the step size in Memetic ABC. The proposed algorithm is named as
Randomized Memetic ABC (RMABC). In RMABC, new solutions are generated nearby
the best so far solution and it helps to increase the exploitation capability
of Memetic ABC. The experiments on some test problems of different complexities
and one well known engineering optimization application show that the proposed
algorithm outperforms over Memetic ABC (MeABC) and some other variant of ABC
algorithm(like Gbest guided ABC (GABC),Hooke Jeeves ABC (HJABC), Best-So-Far
ABC (BSFABC) and Modified ABC (MABC) in case of almost all the problems.
Memetic Search in Differential Evolution Algorithm
Authors: Sandeep Kumar, Vivek Kumar Sharma, Rajani Kumari
Date: 2014-08-01 00:00:00
Tasks: Evolutionary Algorithms, Position

Differential Evolution (DE) is a renowned optimization stratagem that can
easily solve nonlinear and comprehensive problems. DE is a well known and
uncomplicated population based probabilistic approach for comprehensive
optimization. It has apparently outperformed a number of Evolutionary
Algorithms and further search heuristics in the vein of Particle Swarm
Optimization at what time of testing over both yardstick and actual world
problems. Nevertheless, DE, like other probabilistic optimization algorithms,
from time to time exhibits precipitate convergence and stagnates at suboptimal
position. In order to stay away from stagnation behavior while maintaining an
excellent convergence speed, an innovative search strategy is introduced, named
memetic search in DE. In the planned strategy, positions update equation
customized as per a memetic search stratagem. In this strategy a better
solution participates more times in the position modernize procedure. The
position update equation is inspired from the memetic search in artificial bee
colony algorithm. The proposed strategy is named as Memetic Search in
Differential Evolution (MSDE). To prove efficiency and efficacy of MSDE, it is
tested over 8 benchmark optimization problems and three real world optimization
problems. A comparative analysis has also been carried out among proposed MSDE
and original DE. Results show that the anticipated algorithm go one better than
the basic DE and its recent deviations in a good number of the experiments.
Conditional Restricted Boltzmann Machines for Cold Start Recommendations
Authors: Jiankou Li, Wei zhang
Date: 2014-08-01 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

Restricted Boltzman Machines (RBMs) have been successfully used in
recommender systems. However, as with most of other collaborative filtering
techniques, it cannot solve cold start problems for there is no rating for a
new item. In this paper, we first apply conditional RBM (CRBM) which could take
extra information into account and show that CRBM could solve cold start
problem very well, especially for rating prediction task. CRBM naturally
combine the content and collaborative data under a single framework which could
be fitted effectively. Experiments show that CRBM can be compared favourably
with matrix factorization models, while hidden features learned from the former
models are more easy to be interpreted.
Bayesian nonparametric Plackett-Luce models for the analysis of preferences for college degree programmes
Authors: François Caron, Yee Whye Teh, Thomas Brendan Murphy
Date: 2012-11-21 00:00:00
Tasks: Clustering

In this paper we propose a Bayesian nonparametric model for clustering
partial ranking data. We start by developing a Bayesian nonparametric extension
of the popular Plackett-Luce choice model that can handle an infinite number of
choice items. Our framework is based on the theory of random atomic measures,
with the prior specified by a completely random measure. We characterise the
posterior distribution given data, and derive a simple and effective Gibbs
sampler for posterior simulation. We then develop a Dirichlet process mixture
extension of our model and apply it to investigate the clustering of
preferences for college degree programmes amongst Irish secondary school
graduates. The existence of clusters of applicants who have similar preferences
for degree programmes is established and we determine that subject matter and
geographical location of the third level institution characterise these
clusters.
A Framework for learning multi-agent dynamic formation strategy in real-time applications
Authors: Mehrab Norouzitallab, Valiallah Monajjemi, Saeed Shiry Ghidary, Mohammad Bagher Menhaj
Date: 2014-08-01 00:00:00

Formation strategy is one of the most important parts of many multi-agent
systems with many applications in real world problems. In this paper, a
framework for learning this task in a limited domain (restricted environment)
is proposed. In this framework, agents learn either directly by observing an
expert behavior or indirectly by observing other agents or objects behavior.
First, a group of algorithms for learning formation strategy based on limited
features will be presented. Due to distributed and complex nature of many
multi-agent systems, it is impossible to include all features directly in the
learning process; thus, a modular scheme is proposed in order to reduce the
number of features. In this method, some important features have indirect
influence in learning instead of directly involving them as input features.
This framework has the ability to dynamically assign a group of positions to a
group of agents to improve system performance. In addition, it can change the
formation strategy when the context changes. Finally, this framework is able to
automatically produce many complex and flexible formation strategy algorithms
without directly involving an expert to present and implement such complex
algorithms.
Thurstonian Boltzmann Machines: Learning from Multiple Inequalities
Authors: Truyen Tran, Dinh Phung, Svetha Venkatesh
Date: 2014-08-01 00:00:00
Tasks: Collaborative Filtering, Handwritten Digit Recognition

We introduce Thurstonian Boltzmann Machines (TBM), a unified architecture
that can naturally incorporate a wide range of data inputs at the same time.
Our motivation rests in the Thurstonian view that many discrete data types can
be considered as being generated from a subset of underlying latent continuous
variables, and in the observation that each realisation of a discrete type
imposes certain inequalities on those variables. Thus learning and inference in
TBM reduce to making sense of a set of inequalities. Our proposed TBM naturally
supports the following types: Gaussian, intervals, censored, binary,
categorical, muticategorical, ordinal, (in)-complete rank with and without
ties. We demonstrate the versatility and capacity of the proposed model on
three applications of very different natures; namely handwritten digit
recognition, collaborative filtering and complex social survey analysis.
Cumulative Restricted Boltzmann Machines for Ordinal Matrix Data Analysis
Authors: Truyen Tran, Dinh Phung, Svetha Venkatesh
Date: 2014-07-31 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

Ordinal data is omnipresent in almost all multiuser-generated feedback -
questionnaires, preferences etc. This paper investigates modelling of ordinal
data with Gaussian restricted Boltzmann machines (RBMs). In particular, we
present the model architecture, learning and inference procedures for both
vector-variate and matrix-variate ordinal data. We show that our model is able
to capture latent opinion profile of citizens around the world, and is
competitive against state-of-art collaborative filtering techniques on
large-scale public datasets. The model thus has the potential to extend
application of RBMs to diverse domains such as recommendation systems, product
reviews and expert assessments.
Learning From Ordered Sets and Applications in Collaborative Ranking
Authors: Truyen Tran, Dinh Phung, Svetha Venkatesh
Date: 2014-07-31 00:00:00
Tasks: Collaborative Filtering, Collaborative Ranking

Ranking over sets arise when users choose between groups of items. For
example, a group may be of those movies deemed $5$ stars to them, or a
customized tour package. It turns out, to model this data type properly, we
need to investigate the general combinatorics problem of partitioning a set and
ordering the subsets. Here we construct a probabilistic log-linear model over a
set of ordered subsets. Inference in this combinatorial space is highly
challenging: The space size approaches $(N!/2)6.93145^{N+1}$ as $N$ approaches
infinity. We propose a \texttt{split-and-merge} Metropolis-Hastings procedure
that can explore the state-space efficiently. For discovering hidden aspects in
the data, we enrich the model with latent binary variables so that the
posteriors can be efficiently evaluated. Finally, we evaluate the proposed
model on large-scale collaborative filtering tasks and demonstrate that it is
competitive against state-of-the-art methods.