pwc-archive/papers-with-abstracts huggingface dataset:
Spectral Correlation Hub Screening of Multivariate Time Series
Authors: Hamed Firouzi, Dennis Wei, Alfred O. Hero III
Date: 2014-03-13 00:00:00
Tasks: Time Series, Time Series Analysis

This chapter discusses correlation analysis of stationary multivariate
Gaussian time series in the spectral or Fourier domain. The goal is to identify
the hub time series, i.e., those that are highly correlated with a specified
number of other time series. We show that Fourier components of the time series
at different frequencies are asymptotically statistically independent. This
property permits independent correlation analysis at each frequency,
alleviating the computational and statistical challenges of high-dimensional
time series. To detect correlation hubs at each frequency, an existing
correlation screening method is extended to the complex numbers to accommodate
complex-valued Fourier components. We characterize the number of hub
discoveries at specified correlation and degree thresholds in the regime of
increasing dimension and fixed sample size. The theory specifies appropriate
thresholds to apply to sample correlation matrices to detect hubs and also
allows statistical significance to be attributed to hub discoveries. Numerical
results illustrate the accuracy of the theory and the usefulness of the
proposed spectral framework.
A comparison of linear and non-linear calibrations for speaker recognition
Authors: Niko Brümmer, Albert Swart, David van Leeuwen
Date: 2014-02-11 00:00:00
Tasks: Speaker Recognition

In recent work on both generative and discriminative score to
log-likelihood-ratio calibration, it was shown that linear transforms give good
accuracy only for a limited range of operating points. Moreover, these methods
required tailoring of the calibration training objective functions in order to
target the desired region of best accuracy. Here, we generalize the linear
recipes to non-linear ones. We experiment with a non-linear, non-parametric,
discriminative PAV solution, as well as parametric, generative,
maximum-likelihood solutions that use Gaussian, Student's T and
normal-inverse-Gaussian score distributions. Experiments on NIST SRE'12 scores
suggest that the non-linear methods provide wider ranges of optimal accuracy
and can be trained without having to resort to objective function tailoring.
Stopping Criteria in Contrastive Divergence: Alternatives to the Reconstruction Error
Authors: David Buchaca, Enrique Romero, Ferran Mazzanti, Jordi Delgado
Date: 2013-12-20 00:00:00

Restricted Boltzmann Machines (RBMs) are general unsupervised learning
devices to ascertain generative models of data distributions. RBMs are often
trained using the Contrastive Divergence learning algorithm (CD), an
approximation to the gradient of the data log-likelihood. A simple
reconstruction error is often used to decide whether the approximation provided
by the CD algorithm is good enough, though several authors (Schulz et al.,
2010; Fischer & Igel, 2010) have raised doubts concerning the feasibility of
this procedure. However, not many alternatives to the reconstruction error have
been used in the literature. In this manuscript we investigate simple
alternatives to the reconstruction error in order to detect as soon as possible
the decrease in the log-likelihood during learning.
A Dynamic Near-Optimal Algorithm for Online Linear Programming
Authors: Shipra Agrawal, Zizhuo Wang, Yinyu Ye
Date: 2009-11-16 00:00:00
Tasks: Management

A natural optimization model that formulates many online resource allocation
and revenue management problems is the online linear program (LP) in which the
constraint matrix is revealed column by column along with the corresponding
objective coefficient. In such a model, a decision variable has to be set each
time a column is revealed without observing the future inputs and the goal is
to maximize the overall objective function. In this paper, we provide a
near-optimal algorithm for this general class of online problems under the
assumption of random order of arrival and some mild conditions on the size of
the LP right-hand-side input. Specifically, our learning-based algorithm works
by dynamically updating a threshold price vector at geometric time intervals,
where the dual prices learned from the revealed columns in the previous period
are used to determine the sequential decisions in the current period. Due to
the feature of dynamic learning, the competitiveness of our algorithm improves
over the past study of the same problem. We also present a worst-case example
showing that the performance of our algorithm is near-optimal.
Power System Parameters Forecasting Using Hilbert-Huang Transform and Machine Learning
Authors: Victor Kurbatsky, Nikita Tomin, Vadim Spiryaev, Paul Leahy, Denis Sidorov, Alexei Zhukov
Date: 2014-04-09 00:00:00
Tasks: BIG-bench Machine Learning, Hybrid Machine Learning, Time Series, Time Series Analysis

A novel hybrid data-driven approach is developed for forecasting power system
parameters with the goal of increasing the efficiency of short-term forecasting
studies for non-stationary time-series. The proposed approach is based on mode
decomposition and a feature analysis of initial retrospective data using the
Hilbert-Huang transform and machine learning algorithms. The random forests and
gradient boosting trees learning techniques were examined. The decision tree
techniques were used to rank the importance of variables employed in the
forecasting models. The Mean Decrease Gini index is employed as an impurity
function. The resulting hybrid forecasting models employ the radial basis
function neural network and support vector regression. Apart from introduction
and references the paper is organized as follows. The section 2 presents the
background and the review of several approaches for short-term forecasting of
power system parameters. In the third section a hybrid machine learning-based
algorithm using Hilbert-Huang transform is developed for short-term forecasting
of power system parameters. Fourth section describes the decision tree learning
algorithms used for the issue of variables importance. Finally in section six
the experimental results in the following electric power problems are
presented: active power flow forecasting, electricity price forecasting and for
the wind speed and direction forecasting.
A Networks and Machine Learning Approach to Determine the Best College Coaches of the 20th-21st Centuries
Authors: Tian-Shun Jiang, Zachary Polizzi, Christopher Yuan
Date: 2014-04-08 00:00:00

Our objective is to find the five best college sports coaches of past century
for three different sports. We decided to look at men's basketball, football,
and baseball. We wanted to use an approach that could definitively determine
team skill from the games played, and then use a machine-learning algorithm to
calculate the correct coach skills for each team in a given year. We created a
networks-based model to calculate team skill from historical game data. A
digraph was created for each year in each sport. Nodes represented teams, and
edges represented a game played between two teams. The arrowhead pointed
towards the losing team. We calculated the team skill of each graph using a
right-hand eigenvector centrality measure. This way, teams that beat good teams
will be ranked higher than teams that beat mediocre teams. The eigenvector
centrality rankings for most years were well correlated with tournament
performance and poll-based rankings. We assumed that the relationship between
coach skill $C_s$, player skill $P_s$, and team skill $T_s$ was $C_s \cdot P_s
= T_s$. We then created a function to describe the probability that a given
score difference would occur based on player skill and coach skill. We
multiplied the probabilities of all edges in the network together to find the
probability that the correct network would occur with any given player skill
and coach skill matrix. We was able to determine player skill as a function of
team skill and coach skill, eliminating the need to optimize two unknown
matrices. The top five coaches in each year were noted, and the top coach of
all time was calculated by dividing the number of times that coach ranked in
the yearly top five by the years said coach had been active.
Outer-Product Hidden Markov Model and Polyphonic MIDI Score Following
Authors: Eita Nakamura, Tomohiko Nakamura, Yasuyuki Saito, Nobutaka Ono, Shigeki Sagayama
Date: 2014-04-08 00:00:00

We present a polyphonic MIDI score-following algorithm capable of following
performances with arbitrary repeats and skips, based on a probabilistic model
of musical performances. It is attractive in practical applications of score
following to handle repeats and skips which may be made arbitrarily during
performances, but the algorithms previously described in the literature cannot
be applied to scores of practical length due to problems with large
computational complexity. We propose a new type of hidden Markov model (HMM) as
a performance model which can describe arbitrary repeats and skips including
performer tendencies on distributed score positions before and after them, and
derive an efficient score-following algorithm that reduces computational
complexity without pruning. A theoretical discussion on how much such
information on performer tendencies improves the score-following results is
given. The proposed score-following algorithm also admits performance mistakes
and is demonstrated to be effective in practical situations by carrying out
evaluations with human performances. The proposed HMM is potentially valuable
for other topics in information processing and we also provide a detailed
description of inference algorithms.
Algorithms for Learning Kernels Based on Centered Alignment
Authors: Corinna Cortes, Mehryar Mohri, Afshin Rostamizadeh
Date: 2012-03-02 00:00:00
Tasks: General Classification, Generalization Bounds, regression

This paper presents new and effective algorithms for learning kernels. In particular, as shown by our empirical results, these algorithms consistently outperform the so-called uniform combination solution that has proven to be difficult to improve upon in the past, as well as other algorithms for learning kernels based on convex combinations of base kernels in both classification and regression. Our algorithms are based on the notion of centered alignment which is used as a similarity measure between kernels or kernel matrices. We present a number of novel algorithmic, theoretical, and empirical results for learning kernels based on our notion of centered alignment. In particular, we describe efficient algorithms for learning a maximum alignment kernel by showing that the problem can be reduced to a simple QP and discuss a one-stage algorithm for learning both a kernel and a hypothesis based on that kernel using an alignment-based regularization. Our theoretical results include a novel concentration bound for centered alignment between kernel matrices, the proof of the existence of effective predictors for kernels with high alignment, both for classification and for regression, and the proof of stability-based generalization bounds for a broad family of algorithms for learning kernels based on centered alignment. We also report the results of experiments with our centered alignment-based algorithms in both classification and regression.
Data mining for censored time-to-event data: A Bayesian network model for predicting cardiovascular risk from electronic health record data
Authors: Sunayan Bandyopadhyay, Julian Wolfson, David M. Vock, Gabriela Vazquez-Benitez, Gediminas Adomavicius, Mohamed Elidrisi, Paul E. Johnson, Patrick J. O'Connor
Date: 2014-04-08 00:00:00
Tasks: BIG-bench Machine Learning

Models for predicting the risk of cardiovascular events based on individual
patient characteristics are important tools for managing patient care. Most
current and commonly used risk prediction models have been built from carefully
selected epidemiological cohorts. However, the homogeneity and limited size of
such cohorts restricts the predictive power and generalizability of these risk
models to other populations. Electronic health data (EHD) from large health
care systems provide access to data on large, heterogeneous, and
contemporaneous patient populations. The unique features and challenges of EHD,
including missing risk factor information, non-linear relationships between
risk factors and cardiovascular event outcomes, and differing effects from
different patient subgroups, demand novel machine learning approaches to risk
model development. In this paper, we present a machine learning approach based
on Bayesian networks trained on EHD to predict the probability of having a
cardiovascular event within five years. In such data, event status may be
unknown for some individuals as the event time is right-censored due to
disenrollment and incomplete follow-up. Since many traditional data mining
methods are not well-suited for such data, we describe how to modify both
modelling and assessment techniques to account for censored observation times.
We show that our approach can lead to better predictive performance than the
Cox proportional hazards model (i.e., a regression-based approach commonly used
for censored, time-to-event data) or a Bayesian network with {\em{ad hoc}}
approaches to right-censoring. Our techniques are motivated by and illustrated
on data from a large U.S. Midwestern health care system.
The NNN Formalization: Review and Development of Guideline Specification in the Care Domain
Authors: Georg Kaes, Jürgen Manger, Stefanie Rinderle-Ma, Ralph Vigne
Date: 2014-04-08 00:00:00

Due to an ageing society, it can be expected that less nursing personnel will
be responsible for an increasing number of patients in the future. One way to
address this challenge is to provide system-based support for nursing personnel
in creating, executing, and adapting patient care processes. In care practice,
these processes are following the general care process definition and
individually specified according to patient-specific data as well as diagnoses
and guidelines from the NANDA, NIC, and NOC (NNN) standards. In addition,
adaptations to running patient processes become necessary frequently and are to
be conducted by nursing personnel including NNN knowledge. In order to provide
semi-automatic support for design and adaption of care processes, a
formalization of NNN knowledge is indispensable. This technical report presents
the NNN formalization that is developed targeting at goals such as
completeness, flexibility, and later exploitation for creating and adapting
patient care processes. The formalization also takes into consideration an
extensive evaluation of existing formalization standards for clinical
guidelines. The NNN formalization as well as its usage are evaluated based on
case study FATIGUE.
Minimum $n$-Rank Approximation via Iterative Hard Thresholding
Authors: Min Zhang, Lei Yang, Zheng-Hai Huang
Date: 2013-11-18 00:00:00
Tasks: Image Inpainting, Video Inpainting

The problem of recovering a low $n$-rank tensor is an extension of sparse
recovery problem from the low dimensional space (matrix space) to the high
dimensional space (tensor space) and has many applications in computer vision
and graphics such as image inpainting and video inpainting. In this paper, we
consider a new tensor recovery model, named as minimum $n$-rank approximation
(MnRA), and propose an appropriate iterative hard thresholding algorithm with
giving the upper bound of the $n$-rank in advance. The convergence analysis of
the proposed algorithm is also presented. Particularly, we show that for the
noiseless case, the linear convergence with rate $\frac{1}{2}$ can be obtained
for the proposed algorithm under proper conditions. Additionally, combining an
effective heuristic for determining $n$-rank, we can also apply the proposed
algorithm to solve MnRA when $n$-rank is unknown in advance. Some preliminary
numerical results on randomly generated and real low $n$-rank tensor completion
problems are reported, which show the efficiency of the proposed algorithms.
A Naive Bayes machine learning approach to risk prediction using censored, time-to-event data
Authors: Julian Wolfson, Sunayan Bandyopadhyay, Mohamed Elidrisi, Gabriela Vazquez-Benitez, Donald Musgrove, Gediminas Adomavicius, Paul Johnson, Patrick O'Connor
Date: 2014-04-08 00:00:00
Tasks: BIG-bench Machine Learning

Predicting an individual's risk of experiencing a future clinical outcome is
a statistical task with important consequences for both practicing clinicians
and public health experts. Modern observational databases such as electronic
health records (EHRs) provide an alternative to the longitudinal cohort studies
traditionally used to construct risk models, bringing with them both
opportunities and challenges. Large sample sizes and detailed covariate
histories enable the use of sophisticated machine learning techniques to
uncover complex associations and interactions, but observational databases are
often ``messy,'' with high levels of missing data and incomplete patient
follow-up. In this paper, we propose an adaptation of the well-known Naive
Bayes (NB) machine learning approach for classification to time-to-event
outcomes subject to censoring. We compare the predictive performance of our
method to the Cox proportional hazards model which is commonly used for risk
prediction in healthcare populations, and illustrate its application to
prediction of cardiovascular risk using an EHR dataset from a large Midwest
integrated healthcare system.
Rational Counterfactuals
Authors: Tshilidzi Marwala
Date: 2014-04-08 00:00:00
Tasks: counterfactual, Decision Making

This paper introduces the concept of rational countefactuals which is an idea
of identifying a counterfactual from the factual (whether perceived or real)
that maximizes the attainment of the desired consequent. In counterfactual
thinking if we have a factual statement like: Saddam Hussein invaded Kuwait and
consequently George Bush declared war on Iraq then its counterfactuals is: If
Saddam Hussein did not invade Kuwait then George Bush would not have declared
war on Iraq. The theory of rational counterfactuals is applied to identify the
antecedent that gives the desired consequent necessary for rational decision
making. The rational countefactual theory is applied to identify the values of
variables Allies, Contingency, Distance, Major Power, Capability, Democracy, as
well as Economic Interdependency that gives the desired consequent Peace.
Efficiency of conformalized ridge regression
Authors: Evgeny Burnaev, Vladimir Vovk
Date: 2014-04-08 00:00:00
Tasks: Conformal Prediction, Prediction, Prediction Intervals, regression

Conformal prediction is a method of producing prediction sets that can be
applied on top of a wide range of prediction algorithms. The method has a
guaranteed coverage probability under the standard IID assumption regardless of
whether the assumptions (often considerably more restrictive) of the underlying
algorithm are satisfied. However, for the method to be really useful it is
desirable that in the case where the assumptions of the underlying algorithm
are satisfied, the conformal predictor loses little in efficiency as compared
with the underlying algorithm (whereas being a conformal predictor, it has the
stronger guarantee of validity). In this paper we explore the degree to which
this additional requirement of efficiency is satisfied in the case of Bayesian
ridge regression; we find that asymptotically conformal prediction sets differ
little from ridge regression prediction intervals when the standard Bayesian
assumptions are satisfied.
Extracting a bilingual semantic grammar from FrameNet-annotated corpora
Authors: Dana Dannélls, Normunds Grūzītis
Date: 2014-04-08 00:00:00

We present the creation of an English-Swedish FrameNet-based grammar in
Grammatical Framework. The aim of this research is to make existing framenets
computationally accessible for multilingual natural language applications via a
common semantic grammar API, and to facilitate the porting of such grammar to
other languages. In this paper, we describe the abstract syntax of the semantic
grammar while focusing on its automatic extraction possibilities. We have
extracted a shared abstract syntax from ~58,500 annotated sentences in Berkeley
FrameNet (BFN) and ~3,500 annotated sentences in Swedish FrameNet (SweFN). The
abstract syntax defines 769 frame-specific valence patterns that cover 77.8%
examples in BFN and 74.9% in SweFN belonging to the shared set of 471 frames.
As a side result, we provide a unified method for comparing semantic and
syntactic valence patterns across framenets.
Entropy Computation of Document Images in Run-Length Compressed Domain
Authors: P. Nagabhushan, Mohammed Javed, B. B. Chaudhuri
Date: 2014-04-08 00:00:00
Tasks: Retrieval

Compression of documents, images, audios and videos have been traditionally
practiced to increase the efficiency of data storage and transfer. However, in
order to process or carry out any analytical computations, decompression has
become an unavoidable pre-requisite. In this research work, we have attempted
to compute the entropy, which is an important document analytic directly from
the compressed documents. We use Conventional Entropy Quantifier (CEQ) and
Spatial Entropy Quantifiers (SEQ) for entropy computations [1]. The entropies
obtained are useful in applications like establishing equivalence, word
spotting and document retrieval. Experiments have been performed with all the
data sets of [1], at character, word and line levels taking compressed
documents in run-length compressed domain. The algorithms developed are
computational and space efficient, and results obtained match 100% with the
results reported in [1].
A Permutation Approach for Selecting the Penalty Parameter in Penalized Model Selection
Authors: Jeremy Sabourin, William Valdar, Andrew Nobel
Date: 2014-04-08 00:00:00
Tasks: Model Selection, Variable Selection

We describe a simple, efficient, permutation based procedure for selecting
the penalty parameter in the LASSO. The procedure, which is intended for
applications where variable selection is the primary focus, can be applied in a
variety of structural settings, including generalized linear models. We briefly
discuss connections between permutation selection and existing theory for the
LASSO. In addition, we present a simulation study and an analysis of three real
data sets in which permutation selection is compared with cross-validation
(CV), the Bayesian information criterion (BIC), and a selection method based on
recently developed testing procedures for the LASSO.
Automatic Tracker Selection w.r.t Object Detection Performance
Authors: Duc Phu Chau, François Bremond, Monique Thonnat, Slawomir Bak
Date: 2014-04-08 00:00:00
Tasks: Multi-Object Tracking, Object, object-detection, Object Detection, Object Tracking

The tracking algorithm performance depends on video content. This paper
presents a new multi-object tracking approach which is able to cope with video
content variations. First the object detection is improved using Kanade-
Lucas-Tomasi (KLT) feature tracking. Second, for each mobile object, an
appropriate tracker is selected among a KLT-based tracker and a discriminative
appearance-based tracker. This selection is supported by an online tracking
evaluation. The approach has been experimented on three public video datasets.
The experimental results show a better performance of the proposed approach
compared to recent state of the art trackers.