pwc-archive/papers-with-abstracts huggingface dataset:
Incremental Clustering: The Case for Extra Clusters
Authors: Margareta Ackerman, Sanjoy Dasgupta
Date: 2014-06-24 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: Clustering

The explosion in the amount of data available for analysis often necessitates
a transition from batch to incremental clustering methods, which process one
element at a time and typically store only a small subset of the data. In this
paper, we initiate the formal analysis of incremental clustering methods
focusing on the types of cluster structure that they are able to detect. We
find that the incremental setting is strictly weaker than the batch model,
proving that a fundamental class of cluster structures that can readily be
detected in the batch setting is impossible to identify using any incremental
method. Furthermore, we show how the limitations of incremental clustering can
be overcome by allowing additional clusters.
Image patch analysis and clustering of sunspots: a dimensionality reduction approach
Authors: Kevin R. Moon, Jimmy J. Li, Veronique Delouille, Fraser Watson, Alfred O. Hero III
Date: 2014-06-24 00:00:00
Tasks: Clustering, Dimensionality Reduction

Sunspots, as seen in white light or continuum images, are associated with
regions of high magnetic activity on the Sun, visible on magnetogram images.
Their complexity is correlated with explosive solar activity and so classifying
these active regions is useful for predicting future solar activity. Current
classification of sunspot groups is visually based and suffers from bias.
Supervised learning methods can reduce human bias but fail to optimally
capitalize on the information present in sunspot images. This paper uses two
image modalities (continuum and magnetogram) to characterize the spatial and
modal interactions of sunspot and magnetic active region images and presents a
new approach to cluster the images. Specifically, in the framework of image
patch analysis, we estimate the number of intrinsic parameters required to
describe the spatial and modal dependencies, the correlation between the two
modalities and the corresponding spatial patterns, and examine the phenomena at
different scales within the images. To do this, we use linear and nonlinear
intrinsic dimension estimators, canonical correlation analysis, and
multiresolution analysis of intrinsic dimension.
A multilevel thresholding algorithm using Electromagnetism Optimization
Authors: Diego Oliva, Erik Cuevas, Gonzalo Pajares, Daniel Zaldivar, Valentin Osuna
Date: 2014-06-24 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Segmentation is one of the most important tasks in image processing. It
consist in classify the pixels into two or more groups depending on their
intensity levels and a threshold value. The quality of the segmentation depends
on the method applied to select the threshold. The use of the classical
implementations for multilevel thresholding is computationally expensive since
they exhaustively search the best values to optimize the objective function.
Under such conditions, the use of optimization evolutionary approaches has been
extended. The Electromagnetism Like algorithm (EMO) is an evolutionary method
which mimics the attraction repulsion mechanism among charges to evolve the
members of a population. Different to other algorithms, EMO exhibits
interesting search capabilities whereas maintains a low computational overhead.
In this paper, a multilevel thresholding (MT) algorithm based on the EMO is
introduced. The approach combines the good search capabilities of EMO algorithm
with objective functions proposed by the popular MT methods of Otsu and Kapur.
The algorithm takes random samples from a feasible search space inside the
image histogram. Such samples build each particle in the EMO context whereas
its quality is evaluated considering the objective that is function employed by
the Otsu or Kapur method. Guided by these objective values the set of candidate
solutions are evolved through the EMO operators until an optimal solution is
found. The approach generates a multilevel segmentation algorithm which can
effectively identify the threshold values of a digital image in a reduced
number of iterations. Experimental results show performance evidence of the
implementation of EMO for digital image segmentation.
Dense Correspondences Across Scenes and Scales
Authors: Moria Tau, Tal Hassner
Date: 2014-06-24 00:00:00

We seek a practical method for establishing dense correspondences between two
images with similar content, but possibly different 3D scenes. One of the
challenges in designing such a system is the local scale differences of objects
appearing in the two images. Previous methods often considered only small
subsets of image pixels; matching only pixels for which stable scales may be
reliably estimated. More recently, others have considered dense
correspondences, but with substantial costs associated with generating, storing
and matching scale invariant descriptors. Our work here is motivated by the
observation that pixels in the image have contexts -- the pixels around them --
which may be exploited in order to estimate local scales reliably and
repeatably. Specifically, we make the following contributions. (i) We show that
scales estimated in sparse interest points may be propagated to neighboring
pixels where this information cannot be reliably determined. Doing so allows
scale invariant descriptors to be extracted anywhere in the image, not just in
detected interest points. (ii) We present three different means for propagating
this information: using only the scales at detected interest points, using the
underlying image information to guide the propagation of this information
across each image, separately, and using both images simultaneously. Finally,
(iii), we provide extensive results, both qualitative and quantitative,
demonstrating that accurate dense correspondences can be obtained even between
very different images, with little computational costs beyond those required by
existing methods.
Studying Collective Human Decision Making and Creativity with Evolutionary Computation
Authors: Hiroki Sayama, Shelley D. Dionne
Date: 2014-06-24 00:00:00
Tasks: Decision Making

We report a summary of our interdisciplinary research project "Evolutionary
Perspective on Collective Decision Making" that was conducted through close
collaboration between computational, organizational and social scientists at
Binghamton University. We redefined collective human decision making and
creativity as evolution of ecologies of ideas, where populations of ideas
evolve via continual applications of evolutionary operators such as
reproduction, recombination, mutation, selection, and migration of ideas, each
conducted by participating humans. Based on this evolutionary perspective, we
generated hypotheses about collective human decision making using agent-based
computer simulations. The hypotheses were then tested through several
experiments with real human subjects. Throughout this project, we utilized
evolutionary computation (EC) in non-traditional ways---(1) as a theoretical
framework for reinterpreting the dynamics of idea generation and selection, (2)
as a computational simulation model of collective human decision making
processes, and (3) as a research tool for collecting high-resolution
experimental data of actual collaborative design and decision making from human
subjects. We believe our work demonstrates untapped potential of EC for
interdisciplinary research involving human and social dynamics.
Image Completion for View Synthesis Using Markov Random Fields and Efficient Belief Propagation
Authors: Julian Habigt, Klaus Diepold
Date: 2014-06-24 00:00:00

View synthesis is a process for generating novel views from a scene which has
been recorded with a 3-D camera setup. It has important applications in 3-D
post-production and 2-D to 3-D conversion. However, a central problem in the
generation of novel views lies in the handling of disocclusions. Background
content, which was occluded in the original view, may become unveiled in the
synthesized view. This leads to missing information in the generated view which
has to be filled in a visually plausible manner. We present an inpainting
algorithm for disocclusion filling in synthesized views based on Markov random
fields and efficient belief propagation. We compare the result to two
state-of-the-art algorithms and demonstrate a significant improvement in image
quality.
On Soft Power Diagrams
Authors: Steffen Borgwardt
Date: 2013-07-15 00:00:00
Tasks: Clustering, Outlier Detection

Many applications in data analysis begin with a set of points in a Euclidean
space that is partitioned into clusters. Common tasks then are to devise a
classifier deciding which of the clusters a new point is associated to, finding
outliers with respect to the clusters, or identifying the type of clustering
used for the partition.
  One of the common kinds of clusterings are (balanced) least-squares
assignments with respect to a given set of sites. For these, there is a
'separating power diagram' for which each cluster lies in its own cell.
  In the present paper, we aim for efficient algorithms for outlier detection
and the computation of thresholds that measure how similar a clustering is to a
least-squares assignment for fixed sites. For this purpose, we devise a new
model for the computation of a 'soft power diagram', which allows a soft
separation of the clusters with 'point counting properties'; e.g. we are able
to prescribe how many points we want to classify as outliers.
  As our results hold for a more general non-convex model of free sites, we
describe it and our proofs in this more general way. Its locally optimal
solutions satisfy the aforementioned point counting properties. For our target
applications that use fixed sites, our algorithms are efficiently solvable to
global optimality by linear programming.
Recurrent Models of Visual Attention
Authors: Volodymyr Mnih, Nicolas Heess, Alex Graves, Koray Kavukcuoglu
Date: 2014-06-24 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: Hard Attention, image-classification, Image Classification, Reinforcement Learning, Translation

Applying convolutional neural networks to large images is computationally
expensive because the amount of computation scales linearly with the number of
image pixels. We present a novel recurrent neural network model that is capable
of extracting information from an image or video by adaptively selecting a
sequence of regions or locations and only processing the selected regions at
high resolution. Like convolutional neural networks, the proposed model has a
degree of translation invariance built-in, but the amount of computation it
performs can be controlled independently of the input image size. While the
model is non-differentiable, it can be trained using reinforcement learning
methods to learn task-specific policies. We evaluate our model on several image
classification tasks, where it significantly outperforms a convolutional neural
network baseline on cluttered images, and on a dynamic visual control problem,
where it learns to track a simple object without an explicit training signal
for doing so.
Saccadic Eye Movements and the Generalized Pareto Distribution
Authors: Reiner Lenz
Date: 2014-06-24 00:00:00

We describe a statistical analysis of the eye tracker measurements in a
database with 15 observers viewing 1003 images under free-viewing conditions.
In contrast to the common approach of investigating the properties of the
fixation points we analyze the properties of the transition phases between
fixations. We introduce hyperbolic geometry as a tool to measure the step
length between consecutive eye positions. We show that the step lengths,
measured in hyperbolic and euclidean geometry, follow a generalized Pareto
distribution. The results based on the hyperbolic distance are more robust than
those based on euclidean geometry. We show how the structure of the space of
generalized Pareto distributions can be used to characterize and identify
individual observers.
Combining predictions from linear models when training and test inputs differ
Authors: Thijs van Ommen
Date: 2014-06-24 00:00:00

Methods for combining predictions from different models in a supervised
learning setting must somehow estimate/predict the quality of a model's
predictions at unknown future inputs. Many of these methods (often implicitly)
make the assumption that the test inputs are identical to the training inputs,
which is seldom reasonable. By failing to take into account that prediction
will generally be harder for test inputs that did not occur in the training
set, this leads to the selection of too complex models. Based on a novel,
unbiased expression for KL divergence, we propose XAIC and its special case
FAIC as versions of AIC intended for prediction that use different degrees of
knowledge of the test inputs. Both methods substantially differ from and may
outperform all the known versions of AIC even when the training and test inputs
are iid, and are especially useful for deterministic inputs and under covariate
shift. Our experiments on linear models suggest that if the test and training
inputs differ substantially, then XAIC and FAIC predictively outperform AIC,
BIC and several other methods including Bayesian model averaging.
Composite Likelihood Estimation for Restricted Boltzmann machines
Authors: Muneki Yasuda, Shun Kataoka, Yuji Waizumi, Kazuyuki Tanaka
Date: 2014-06-24 00:00:00

Learning the parameters of graphical models using the maximum likelihood
estimation is generally hard which requires an approximation. Maximum composite
likelihood estimations are statistical approximations of the maximum likelihood
estimation which are higher-order generalizations of the maximum
pseudo-likelihood estimation. In this paper, we propose a composite likelihood
method and investigate its property. Furthermore, we apply our composite
likelihood method to restricted Boltzmann machines.
A Concise Information-Theoretic Derivation of the Baum-Welch algorithm
Authors: Alireza Nejati, Charles Unsworth
Date: 2014-06-24 00:00:00
Tasks: BIG-bench Machine Learning

We derive the Baum-Welch algorithm for hidden Markov models (HMMs) through an
information-theoretical approach using cross-entropy instead of the Lagrange
multiplier approach which is universal in machine learning literature. The
proposed approach provides a more concise derivation of the Baum-Welch method
and naturally generalizes to multiple observations.
Incorporating Near-Infrared Information into Semantic Image Segmentation
Authors: Neda Salamati, Diane Larlus, Gabriela Csurka, Sabine Süsstrunk
Date: 2014-06-24 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Recent progress in computational photography has shown that we can acquire
near-infrared (NIR) information in addition to the normal visible (RGB) band,
with only slight modifications to standard digital cameras. Due to the
proximity of the NIR band to visible radiation, NIR images share many
properties with visible images. However, as a result of the material dependent
reflection in the NIR part of the spectrum, such images reveal different
characteristics of the scene. We investigate how to effectively exploit these
differences to improve performance on the semantic image segmentation task.
Based on a state-of-the-art segmentation framework and a novel manually
segmented image database (both indoor and outdoor scenes) that contain
4-channel images (RGB+NIR), we study how to best incorporate the specific
characteristics of the NIR response. We show that adding NIR leads to improved
performance for classes that correspond to a specific type of material in both
outdoor and indoor scenes. We also discuss the results with respect to the
physical properties of the NIR response.
Synthesizing Manipulation Sequences for Under-Specified Tasks using Unrolled Markov Random Fields
Authors: Jaeyong Sung, Bart Selman, Ashutosh Saxena
Date: 2013-06-24 00:00:00

Many tasks in human environments require performing a sequence of navigation
and manipulation steps involving objects. In unstructured human environments,
the location and configuration of the objects involved often change in
unpredictable ways. This requires a high-level planning strategy that is robust
and flexible in an uncertain environment. We propose a novel dynamic planning
strategy, which can be trained from a set of example sequences. High level
tasks are expressed as a sequence of primitive actions or controllers (with
appropriate parameters). Our score function, based on Markov Random Field
(MRF), captures the relations between environment, controllers, and their
arguments. By expressing the environment using sets of attributes, the approach
generalizes well to unseen scenarios. We train the parameters of our MRF using
a maximum margin learning method. We provide a detailed empirical validation of
our overall framework demonstrating successful plan strategies for a variety of
tasks.
Generalized Mixability via Entropic Duality
Authors: Mark D. Reid, Rafael M. Frongillo, Robert C. Williamson, Nishant Mehta
Date: 2014-06-24 00:00:00

Mixability is a property of a loss which characterizes when fast convergence
is possible in the game of prediction with expert advice. We show that a key
property of mixability generalizes, and the exp and log operations present in
the usual theory are not as special as one might have thought. In doing this we
introduce a more general notion of $\Phi$-mixability where $\Phi$ is a general
entropy (\ie, any convex function on probabilities). We show how a property
shared by the convex dual of any such entropy yields a natural algorithm (the
minimizer of a regret bound) which, analogous to the classical aggregating
algorithm, is guaranteed a constant regret when used with $\Phi$-mixable
losses. We characterize precisely which $\Phi$ have $\Phi$-mixable losses and
put forward a number of conjectures about the optimality and relationships
between different choices of entropy.
Mining Recurrent Concepts in Data Streams using the Discrete Fourier Transform
Authors: Sakthithasan Sripirakas, Russel Pears
Date: 2014-06-24 00:00:00
Tasks: Meta-Learning

In this research we address the problem of capturing recurring concepts in a
data stream environment. Recurrence capture enables the re-use of previously
learned classifiers without the need for re-learning while providing for better
accuracy during the concept recurrence interval. We capture concepts by
applying the Discrete Fourier Transform (DFT) to Decision Tree classifiers to
obtain highly compressed versions of the trees at concept drift points in the
stream and store such trees in a repository for future use. Our empirical
results on real world and synthetic data exhibiting varying degrees of
recurrence show that the Fourier compressed trees are more robust to noise and
are able to capture recurring concepts with higher precision than a meta
learning approach that chooses to re-use classifiers in their originally
occurring form.
Random Logic Programs: Linear Model
Authors: Kewen Wang, Lian Wen, Kedian Mu
Date: 2014-06-23 00:00:00
Tasks: model

This paper proposes a model, the linear model, for randomly generating logic
programs with low density of rules and investigates statistical properties of
such random logic programs. It is mathematically shown that the average number
of answer sets for a random program converges to a constant when the number of
atoms approaches infinity. Several experimental results are also reported,
which justify the suitability of the linear model. It is also experimentally
shown that, under this model, the size distribution of answer sets for random
programs tends to a normal distribution when the number of atoms is
sufficiently large.
Improved Frame Level Features and SVM Supervectors Approach for the Recogniton of Emotional States from Speech: Application to categorical and dimensional states
Authors: Imen Trabelsi, Dorra Ben Ayed, Noureddine Ellouze
Date: 2014-06-23 00:00:00
Tasks: Emotion Recognition, Speech Emotion Recognition

The purpose of speech emotion recognition system is to classify speakers
utterances into different emotional states such as disgust, boredom, sadness,
neutral and happiness. Speech features that are commonly used in speech emotion
recognition rely on global utterance level prosodic features. In our work, we
evaluate the impact of frame level feature extraction. The speech samples are
from Berlin emotional database and the features extracted from these utterances
are energy, different variant of mel frequency cepstrum coefficients, velocity
and acceleration features.
From Black-Scholes to Online Learning: Dynamic Hedging under Adversarial Environments
Authors: Henry Lam, Zhenming Liu
Date: 2014-06-23 00:00:00

We consider a non-stochastic online learning approach to price financial
options by modeling the market dynamic as a repeated game between the nature
(adversary) and the investor. We demonstrate that such framework yields
analogous structure as the Black-Scholes model, the widely popular option
pricing model in stochastic finance, for both European and American options
with convex payoffs. In the case of non-convex options, we construct
approximate pricing algorithms, and demonstrate that their efficiency can be
analyzed through the introduction of an artificial probability measure, in
parallel to the so-called risk-neutral measure in the finance literature, even
though our framework is completely adversarial. Continuous-time convergence
results and extensions to incorporate price jumps are also presented.
A Novel M-Estimator for Robust PCA
Authors: Teng Zhang, Gilad Lerman
Date: 2011-12-20 00:00:00

We study the basic problem of robust subspace recovery. That is, we assume a
data set that some of its points are sampled around a fixed subspace and the
rest of them are spread in the whole ambient space, and we aim to recover the
fixed underlying subspace. We first estimate "robust inverse sample covariance"
by solving a convex minimization procedure; we then recover the subspace by the
bottom eigenvectors of this matrix (their number correspond to the number of
eigenvalues close to 0). We guarantee exact subspace recovery under some
conditions on the underlying data. Furthermore, we propose a fast iterative
algorithm, which linearly converges to the matrix minimizing the convex
problem. We also quantify the effect of noise and regularization and discuss
many other practical and theoretical issues for improving the subspace recovery
in various settings. When replacing the sum of terms in the convex energy
function (that we minimize) with the sum of squares of terms, we obtain that
the new minimizer is a scaled version of the inverse sample covariance (when
exists). We thus interpret our minimizer and its subspace (spanned by its
bottom eigenvectors) as robust versions of the empirical inverse covariance and
the PCA subspace respectively. We compare our method with many other algorithms
for robust PCA on synthetic and real data sets and demonstrate state-of-the-art
speed and accuracy.
Stationary Mixing Bandits
Authors: Julien Audiffren, Liva Ralaivola
Date: 2014-06-23 00:00:00

We study the bandit problem where arms are associated with stationary
phi-mixing processes and where rewards are therefore dependent: the question
that arises from this setting is that of recovering some independence by
ignoring the value of some rewards. As we shall see, the bandit problem we
tackle requires us to address the exploration/exploitation/independence
trade-off. To do so, we provide a UCB strategy together with a general regret
analysis for the case where the size of the independence blocks (the ignored
rewards) is fixed and we go a step beyond by providing an algorithm that is
able to compute the size of the independence blocks from the data. Finally, we
give an analysis of our bandit problem in the restless case, i.e., in the
situation where the time counters for all mixing processes simultaneously
evolve.