pwc-archive/papers-with-abstracts huggingface dataset:
Online Censoring for Large-Scale Regressions with Application to Streaming Big Data
Authors: Dimitris Berberidis, Vassilis Kekatos, Georgios B. Giannakis
Date: 2015-07-27 00:00:00
Tasks: Dimensionality Reduction, regression

Linear regression is arguably the most prominent among statistical inference
methods, popular both for its simplicity as well as its broad applicability. On
par with data-intensive applications, the sheer size of linear regression
problems creates an ever growing demand for quick and cost efficient solvers.
Fortunately, a significant percentage of the data accrued can be omitted while
maintaining a certain quality of statistical inference with an affordable
computational budget. The present paper introduces means of identifying and
omitting "less informative" observations in an online and data-adaptive
fashion, built on principles of stochastic approximation and data censoring.
First- and second-order stochastic approximation maximum likelihood-based
algorithms for censored observations are developed for estimating the
regression coefficients. Online algorithms are also put forth to reduce the
overall complexity by adaptively performing censoring along with estimation.
The novel algorithms entail simple closed-form updates, and have provable
(non)asymptotic convergence guarantees. Furthermore, specific rules are
investigated for tuning to desired censoring patterns and levels of
dimensionality reduction. Simulated tests on real and synthetic datasets
corroborate the efficacy of the proposed data-adaptive methods compared to
data-agnostic random projection-based alternatives.
Occlusion-Aware Object Localization, Segmentation and Pose Estimation
Authors: Samarth Brahmbhatt, Heni Ben Amor, Henrik Christensen
Date: 2015-07-27 00:00:00
Tasks: 3D Pose Estimation, Object, Object Localization, Pose Estimation, Segmentation

We present a learning approach for localization and segmentation of objects
in an image in a manner that is robust to partial occlusion. Our algorithm
produces a bounding box around the full extent of the object and labels pixels
in the interior that belong to the object. Like existing segmentation aware
detection approaches, we learn an appearance model of the object and consider
regions that do not fit this model as potential occlusions. However, in
addition to the established use of pairwise potentials for encouraging local
consistency, we use higher order potentials which capture information at the
level of im- age segments. We also propose an efficient loss function that
targets both localization and segmentation performance. Our algorithm achieves
13.52% segmentation error and 0.81 area under the false-positive per image vs.
recall curve on average over the challenging CMU Kitchen Occlusion Dataset.
This is a 42.44% decrease in segmentation error and a 16.13% increase in
localization performance compared to the state-of-the-art. Finally, we show
that the visibility labelling produced by our algorithm can make full 3D pose
estimation from a single image robust to occlusion.
AMP: a new time-frequency feature extraction method for intermittent time-series data
Authors: Duncan Barrack, James Goulding, Keith Hopcraft, Simon Preston, Gavin Smith
Date: 2015-07-20 00:00:00
Tasks: Clustering, Time Series, Time Series Analysis

The characterisation of time-series data via their most salient features is
extremely important in a range of machine learning task, not least of all with
regards to classification and clustering. While there exist many feature
extraction techniques suitable for non-intermittent time-series data, these
approaches are not always appropriate for intermittent time-series data, where
intermittency is characterized by constant values for large periods of time
punctuated by sharp and transient increases or decreases in value.
  Motivated by this, we present aggregation, mode decomposition and projection
(AMP) a feature extraction technique particularly suited to intermittent
time-series data which contain time-frequency patterns. For our method all
individual time-series within a set are combined to form a non-intermittent
aggregate. This is decomposed into a set of components which represent the
intrinsic time-frequency signals within the data set. Individual time-series
can then be fit to these components to obtain a set of numerical features that
represent their intrinsic time-frequency patterns. To demonstrate the
effectiveness of AMP, we evaluate against the real word task of clustering
intermittent time-series data. Using synthetically generated data we show that
a clustering approach which uses the features derived from AMP significantly
outperforms traditional clustering methods. Our technique is further
exemplified on a real world data set where AMP can be used to discover
groupings of individuals which correspond to real world sub-populations.
Estimating an Activity Driven Hidden Markov Model
Authors: David A. Meyer, Asif Shakeel
Date: 2015-07-27 00:00:00
Tasks: model

We define a Hidden Markov Model (HMM) in which each hidden state has
time-dependent $\textit{activity levels}$ that drive transitions and emissions,
and show how to estimate its parameters. Our construction is motivated by the
problem of inferring human mobility on sub-daily time scales from, for example,
mobile phone records.
Unification of Fusion Theories, Rules, Filters, Image Fusion and Target Tracking Methods (UFT)
Authors: Florentin Smarandache
Date: 2015-07-27 00:00:00

The author has pledged in various papers, conference or seminar
presentations, and scientific grant applications (between 2004-2015) for the
unification of fusion theories, combinations of fusion rules, image fusion
procedures, filter algorithms, and target tracking methods for more accurate
applications to our real world problems - since neither fusion theory nor
fusion rule fully satisfy all needed applications. For each particular
application, one selects the most appropriate fusion space and fusion model,
then the fusion rules, and the algorithms of implementation. He has worked in
the Unification of the Fusion Theories (UFT), which looks like a cooking
recipe, better one could say like a logical chart for a computer programmer,
but one does not see another method to comprise/unify all things. The
unification scenario presented herein, which is now in an incipient form,
should periodically be updated incorporating new discoveries from the fusion
and engineering research.
Discovery of Shared Semantic Spaces for Multi-Scene Video Query and Summarization
Authors: Xun Xu, Timothy Hospedales, Shaogang Gong
Date: 2015-07-27 00:00:00
Tasks: Scene Understanding, Semantic Similarity, Semantic Textual Similarity, Video Summarization

The growing rate of public space CCTV installations has generated a need for
automated methods for exploiting video surveillance data including scene
understanding, query, behaviour annotation and summarization. For this reason,
extensive research has been performed on surveillance scene understanding and
analysis. However, most studies have considered single scenes, or groups of
adjacent scenes. The semantic similarity between different but related scenes
(e.g., many different traffic scenes of similar layout) is not generally
exploited to improve any automated surveillance tasks and reduce manual effort.
Exploiting commonality, and sharing any supervised annotations, between
different scenes is however challenging due to: Some scenes are totally
un-related -- and thus any information sharing between them would be
detrimental; while others may only share a subset of common activities -- and
thus information sharing is only useful if it is selective. Moreover,
semantically similar activities which should be modelled together and shared
across scenes may have quite different pixel-level appearance in each scene. To
address these issues we develop a new framework for distributed multiple-scene
global understanding that clusters surveillance scenes by their ability to
explain each other's behaviours; and further discovers which subset of
activities are shared versus scene-specific within each cluster. We show how to
use this structured representation of multiple scenes to improve common
surveillance tasks including scene activity understanding, cross-scene
query-by-example, behaviour classification with reduced supervised labelling
requirements, and video summarization. In each case we demonstrate how our
multi-scene model improves on a collection of standard single scene models and
a flat model of all scenes.
Robust Subjective Visual Property Prediction from Crowdsourced Pairwise Labels
Authors: Yanwei Fu, Timothy M. Hospedales, Tao Xiang, Jiechao Xiong, Shaogang Gong, Yizhou Wang, Yuan YAO
Date: 2015-01-25 00:00:00
Tasks: Attribute, Learning-To-Rank, Outlier Detection, Property Prediction

The problem of estimating subjective visual properties from image and video
has attracted increasing interest. A subjective visual property is useful
either on its own (e.g. image and video interestingness) or as an intermediate
representation for visual recognition (e.g. a relative attribute). Due to its
ambiguous nature, annotating the value of a subjective visual property for
learning a prediction model is challenging. To make the annotation more
reliable, recent studies employ crowdsourcing tools to collect pairwise
comparison labels because human annotators are much better at ranking two
images/videos (e.g. which one is more interesting) than giving an absolute
value to each of them separately. However, using crowdsourced data also
introduces outliers. Existing methods rely on majority voting to prune the
annotation outliers/errors. They thus require large amount of pairwise labels
to be collected. More importantly as a local outlier detection method, majority
voting is ineffective in identifying outliers that can cause global ranking
inconsistencies. In this paper, we propose a more principled way to identify
annotation outliers by formulating the subjective visual property prediction
task as a unified robust learning to rank problem, tackling both the outlier
detection and learning to rank jointly. Differing from existing methods, the
proposed method integrates local pairwise comparison labels together to
minimise a cost that corresponds to global inconsistency of ranking order. This
not only leads to better detection of annotation outliers but also enables
learning with extremely sparse annotations. Extensive experiments on various
benchmark datasets demonstrate that our new approach significantly outperforms
state-of-the-arts alternatives.
Requirements for Open-Ended Evolution in Natural and Artificial Systems
Authors: Tim Taylor
Date: 2015-07-27 00:00:00
Tasks: Diversity

Open-ended evolutionary dynamics remains an elusive goal for artificial
evolutionary systems. Many ideas exist in the biological literature beyond the
basic Darwinian requirements of variation, differential reproduction and
inheritance. I argue that these ideas can be seen as aspects of five
fundamental requirements for open-ended evolution: (1) robustly reproductive
individuals, (2) a medium allowing the possible existence of a practically
unlimited diversity of individuals and interactions, (3) individuals capable of
producing more complex offspring, (4) mutational pathways to other viable
individuals, and (5) drive for continued evolution. I briefly discuss
implications of this view for the design of artificial systems with greater
evolutionary potential.
A genetic algorithm for autonomous navigation in partially observable domain
Authors: Maxim Borisyak, Andrey Ustyuzhanin
Date: 2015-07-27 00:00:00
Tasks: Autonomous Navigation

The problem of autonomous navigation is one of the basic problems for
robotics. Although, in general, it may be challenging when an autonomous
vehicle is placed into partially observable domain. In this paper we consider
simplistic environment model and introduce a navigation algorithm based on
Learning Classifier System.
Variational Bayesian strategies for high-dimensional, stochastic design problems
Authors: Phaedon-Stelios Koutsourelakis
Date: 2015-07-24 00:00:00
Tasks: Uncertainty Quantification, Vocal Bursts Intensity Prediction

This paper is concerned with a lesser-studied problem in the context of
model-based, uncertainty quantification (UQ), that of
optimization/design/control under uncertainty. The solution of such problems is
hindered not only by the usual difficulties encountered in UQ tasks (e.g. the
high computational cost of each forward simulation, the large number of random
variables) but also by the need to solve a nonlinear optimization problem
involving large numbers of design variables and potentially constraints. We
propose a framework that is suitable for a large class of such problems and is
based on the idea of recasting them as probabilistic inference tasks. To that
end, we propose a Variational Bayesian (VB) formulation and an iterative
VB-Expectation-Maximization scheme that is also capable of identifying a
low-dimensional set of directions in the design space, along which, the
objective exhibits the largest sensitivity.
  We demonstrate the validity of the proposed approach in the context of two
numerical examples involving $\mathcal{O}(10^3)$ random and design variables.
In all cases considered the cost of the computations in terms of calls to the
forward model was of the order $\mathcal{O}(10^2)$. The accuracy of the
approximations provided is assessed by appropriate information-theoretic
metrics.
A Review of Network Traffic Analysis and Prediction Techniques
Authors: Manish Joshi, Theyazn Hassn Hadi
Date: 2015-07-21 00:00:00
Tasks: Prediction, Traffic Prediction

Analysis and prediction of network traffic has applications in wide
comprehensive set of areas and has newly attracted significant number of
studies. Different kinds of experiments are conducted and summarized to
identify various problems in existing computer network applications. Network
traffic analysis and prediction is a proactive approach to ensure secure,
reliable and qualitative network communication. Various techniques are proposed
and experimented for analyzing network traffic including neural network based
techniques to data mining techniques. Similarly, various Linear and non-linear
models are proposed for network traffic prediction. Several interesting
combinations of network analysis and prediction techniques are implemented to
attain efficient and effective results.
  This paper presents a survey on various such network analysis and traffic
prediction techniques. The uniqueness and rules of previous studies are
investigated. Moreover, various accomplished areas of analysis and prediction
of network traffic have been summed.
Implementing Randomized Matrix Algorithms in Parallel and Distributed Environments
Authors: Jiyan Yang, Xiangrui Meng, Michael W. Mahoney
Date: 2015-02-10 00:00:00
Tasks: regression

In this era of large-scale data, distributed systems built on top of clusters
of commodity hardware provide cheap and reliable storage and scalable
processing of massive data. Here, we review recent work on developing and
implementing randomized matrix algorithms in large-scale parallel and
distributed environments. Randomized algorithms for matrix problems have
received a great deal of attention in recent years, thus far typically either
in theory or in machine learning applications or with implementations on a
single machine. Our main focus is on the underlying theory and practical
implementation of random projection and random sampling algorithms for very
large very overdetermined (i.e., overconstrained) $\ell_1$ and $\ell_2$
regression problems. Randomization can be used in one of two related ways:
either to construct sub-sampled problems that can be solved, exactly or
approximately, with traditional numerical methods; or to construct
preconditioned versions of the original full problem that are easier to solve
with traditional iterative algorithms. Theoretical results demonstrate that in
near input-sparsity time and with only a few passes through the data one can
obtain very strong relative-error approximate solutions, with high probability.
Empirical results highlight the importance of various trade-offs (e.g., between
the time to construct an embedding and the conditioning quality of the
embedding, between the relative importance of computation versus communication,
etc.) and demonstrate that $\ell_1$ and $\ell_2$ regression problems can be
solved to low, medium, or high precision in existing distributed systems on up
to terabyte-sized data.
A Social Spider Algorithm for Solving the Non-convex Economic Load Dispatch Problem
Authors: James J. Q. Yu, Victor O. K. Li
Date: 2015-07-27 00:00:00

Economic Load Dispatch (ELD) is one of the essential components in power
system control and operation. Although conventional ELD formulation can be
solved using mathematical programming techniques, modern power system
introduces new models of the power units which are non-convex,
non-differentiable, and sometimes non-continuous. In order to solve such
non-convex ELD problems, in this paper we propose a new approach based on the
Social Spider Algorithm (SSA). The classical SSA is modified and enhanced to
adapt to the unique characteristics of ELD problems, e.g., valve-point effects,
multi-fuel operations, prohibited operating zones, and line losses. To
demonstrate the superiority of our proposed approach, five widely-adopted test
systems are employed and the simulation results are compared with the
state-of-the-art algorithms. In addition, the parameter sensitivity is
illustrated by a series of simulations. The simulation results show that SSA
can solve ELD problems effectively and efficiently.
Learning (Predictive) Risk Scores in the Presence of Censoring due to Interventions
Authors: Kirill Dyagilev, Suchi Saria
Date: 2015-07-27 00:00:00

A large and diverse set of measurements are regularly collected during a
patient's hospital stay to monitor their health status. Tools for integrating
these measurements into severity scores, that accurately track changes in
illness severity, can improve clinicians ability to provide timely
interventions. Existing approaches for creating such scores either 1) rely on
experts to fully specify the severity score, or 2) train a predictive score,
using supervised learning, by regressing against a surrogate marker of severity
such as the presence of downstream adverse events. The first approach does not
extend to diseases where an accurate score cannot be elicited from experts. The
second approach often produces scores that suffer from bias due to
treatment-related censoring (Paxton, 2013). We propose a novel ranking based
framework for disease severity score learning (DSSL). DSSL exploits the
following key observation: while it is challenging for experts to quantify the
disease severity at any given time, it is often easy to compare the disease
severity at two different times. Extending existing ranking algorithms, DSSL
learns a function that maps a vector of patient's measurements to a scalar
severity score such that the resulting score is temporally smooth and
consistent with the expert's ranking of pairs of disease states. We apply DSSL
to the problem of learning a sepsis severity score using a large, real-world
dataset. The learned scores significantly outperform state-of-the-art clinical
scores in ranking patient states by severity and in early detection of future
adverse events. We also show that the learned disease severity trajectories are
consistent with clinical expectations of disease evolution. Further, using
simulated datasets, we show that DSSL exhibits better generalization
performance to changes in treatment patterns compared to the above approaches.
Differentially Private Analysis of Outliers
Authors: Rina Okada, Kazuto Fukuchi, Kazuya Kakizaki, Jun Sakuma
Date: 2015-07-24 00:00:00
Tasks: Outlier Detection, Sensitivity

This paper investigates differentially private analysis of distance-based
outliers. The problem of outlier detection is to find a small number of
instances that are apparently distant from the remaining instances. On the
other hand, the objective of differential privacy is to conceal presence (or
absence) of any particular instance. Outlier detection and privacy protection
are thus intrinsically conflicting tasks. In this paper, instead of reporting
outliers detected, we present two types of differentially private queries that
help to understand behavior of outliers. One is the query to count outliers,
which reports the number of outliers that appear in a given subspace. Our
formal analysis on the exact global sensitivity of outlier counts reveals that
regular global sensitivity based method can make the outputs too noisy,
particularly when the dimensionality of the given subspace is high. Noting that
the counts of outliers are typically expected to be relatively small compared
to the number of data, we introduce a mechanism based on the smooth upper bound
of the local sensitivity. The other is the query to discovery top-$h$ subspaces
containing a large number of outliers. This task can be naively achieved by
issuing count queries to each subspace in turn. However, the variation of
subspaces can grow exponentially in the data dimensionality. This can cause
serious consumption of the privacy budget. For this task, we propose an
exponential mechanism with a customized score function for subspace discovery.
To the best of our knowledge, this study is the first trial to ensure
differential privacy for distance-based outlier analysis. We demonstrated our
methods with synthesized datasets and real datasets. The experimental results
show that out method achieve better utility compared to the global sensitivity
based methods.
Reduced-Set Kernel Principal Components Analysis for Improving the Training and Execution Speed of Kernel Machines
Authors: Hassan A. Kingravi, Patricio A. Vela, Alexandar Gray
Date: 2015-07-26 00:00:00

This paper presents a practical, and theoretically well-founded, approach to
improve the speed of kernel manifold learning algorithms relying on spectral
decomposition. Utilizing recent insights in kernel smoothing and learning with
integral operators, we propose Reduced Set KPCA (RSKPCA), which also suggests
an easy-to-implement method to remove or replace samples with minimal effect on
the empirical operator. A simple data point selection procedure is given to
generate a substitute density for the data, with accuracy that is governed by a
user-tunable parameter . The effect of the approximation on the quality of the
KPCA solution, in terms of spectral and operator errors, can be shown directly
in terms of the density estimate error and as a function of the parameter . We
show in experiments that RSKPCA can improve both training and evaluation time
of KPCA by up to an order of magnitude, and compares favorably to the
widely-used Nystrom and density-weighted Nystrom methods.
Estimator Selection: End-Performance Metric Aspects
Authors: Dimitrios Katselis, Cristian R. Rojas, Carolyn L. Beck
Date: 2015-07-26 00:00:00

Recently, a framework for application-oriented optimal experiment design has
been introduced. In this context, the distance of the estimated system from the
true one is measured in terms of a particular end-performance metric. This
treatment leads to superior unknown system estimates to classical experiment
designs based on usual pointwise functional distances of the estimated system
from the true one. The separation of the system estimator from the experiment
design is done within this new framework by choosing and fixing the estimation
method to either a maximum likelihood (ML) approach or a Bayesian estimator
such as the minimum mean square error (MMSE). Since the MMSE estimator delivers
a system estimate with lower mean square error (MSE) than the ML estimator for
finite-length experiments, it is usually considered the best choice in practice
in signal processing and control applications. Within the application-oriented
framework a related meaningful question is: Are there end-performance metrics
for which the ML estimator outperforms the MMSE when the experiment is
finite-length? In this paper, we affirmatively answer this question based on a
simple linear Gaussian regression example.