pwc-archive/papers-with-abstracts huggingface dataset:
Dimensionality Detection and Integration of Multiple Data Sources via the GP-LVM
Authors: James Barrett, Anthony C. C. Coolen
Date: 2013-07-01 00:00:00
Tasks: Binary Classification, Model Selection, Retrieval

The Gaussian Process Latent Variable Model (GP-LVM) is a non-linear
probabilistic method of embedding a high dimensional dataset in terms low
dimensional `latent' variables. In this paper we illustrate that maximum a
posteriori (MAP) estimation of the latent variables and hyperparameters can be
used for model selection and hence we can determine the optimal number or
latent variables and the most appropriate model. This is an alternative to the
variational approaches developed recently and may be useful when we want to use
a non-Gaussian prior or kernel functions that don't have automatic relevance
determination (ARD) parameters. Using a second order expansion of the latent
variable posterior we can marginalise the latent variables and obtain an
estimate for the hyperparameter posterior. Secondly, we use the GP-LVM to
integrate multiple data sources by simultaneously embedding them in terms of
common latent variables. We present results from synthetic data to illustrate
the successful detection and retrieval of low dimensional structure from high
dimensional data. We demonstrate that the integration of multiple data sources
leads to more robust performance. Finally, we show that when the data are used
for binary classification tasks we can attain a significant gain in prediction
accuracy when the low dimensional representation is used.
Algorithms of the LDA model [REPORT]
Authors: Jaka Špeh, Andrej Muhič, Jan Rupnik
Date: 2013-07-01 00:00:00
Tasks: Bayesian Inference, model, Variational Inference

We review three algorithms for Latent Dirichlet Allocation (LDA). Two of them
are variational inference algorithms: Variational Bayesian inference and Online
Variational Bayesian inference and one is Markov Chain Monte Carlo (MCMC)
algorithm -- Collapsed Gibbs sampling. We compare their time complexity and
performance. We find that online variational Bayesian inference is the fastest
algorithm and still returns reasonably good results.
A Counterexample for the Validity of Using Nuclear Norm as a Convex Surrogate of Rank
Authors: Hongyang Zhang, Zhouchen Lin, Chao Zhang
Date: 2013-04-23 00:00:00
Tasks: valid

Rank minimization has attracted a lot of attention due to its robustness in
data recovery. To overcome the computational difficulty, rank is often replaced
with nuclear norm. For several rank minimization problems, such a replacement
has been theoretically proven to be valid, i.e., the solution to nuclear norm
minimization problem is also the solution to rank minimization problem.
Although it is easy to believe that such a replacement may not always be valid,
no concrete example has ever been found. We argue that such a validity checking
cannot be done by numerical computation and show, by analyzing the noiseless
latent low rank representation (LatLRR) model, that even for very simple rank
minimization problems the validity may still break down. As a by-product, we
find that the solution to the nuclear norm minimization formulation of LatLRR
is non-unique. Hence the results of LatLRR reported in the literature may be
questionable.
Multilevel Threshold Based Gray Scale Image Segmentation using Cuckoo Search
Authors: Sourav Samantaa, Nilanjan Dey, Poulami Das, Suvojit Acharjee, Sheli Sinha Chaudhuri
Date: 2013-07-01 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Image Segmentation is a technique of partitioning the original image into
some distinct classes. Many possible solutions may be available for segmenting
an image into a certain number of classes, each one having different quality of
segmentation. In our proposed method, multilevel thresholding technique has
been used for image segmentation. A new approach of Cuckoo Search (CS) is used
for selection of optimal threshold value. In other words, the algorithm is used
to achieve the best solution from the initial random threshold values or
solutions and to evaluate the quality of a solution correlation function is
used. Finally, MSE and PSNR are measured to understand the segmentation
quality.
WebSets: Extracting Sets of Entities from the Web Using Unsupervised Information Extraction
Authors: Bhavana Dalvi, William W. Cohen, Jamie Callan
Date: 2013-07-01 00:00:00
Tasks: Clustering, Information Retrieval

We describe a open-domain information extraction method for extracting
concept-instance pairs from an HTML corpus. Most earlier approaches to this
problem rely on combining clusters of distributionally similar terms and
concept-instance pairs obtained with Hearst patterns. In contrast, our method
relies on a novel approach for clustering terms found in HTML tables, and then
assigning concept names to these clusters using Hearst patterns. The method can
be efficiently applied to a large corpus, and experimental results on several
datasets show that our method can accurately extract large numbers of
concept-instance pairs.
Exploratory Learning
Authors: Bhavana Dalvi, William W. Cohen, Jamie Callan
Date: 2013-07-01 00:00:00
Tasks: Clustering

In multiclass semi-supervised learning (SSL), it is sometimes the case that
the number of classes present in the data is not known, and hence no labeled
examples are provided for some classes. In this paper we present variants of
well-known semi-supervised multiclass learning methods that are robust when the
data contains an unknown number of classes. In particular, we present an
"exploratory" extension of expectation-maximization (EM) that explores
different numbers of classes while learning. "Exploratory" SSL greatly improves
performance on three datasets in terms of F1 on the classes with seed examples
i.e., the classes which are expected to be in the data. Our Exploratory EM
algorithm also outperforms a SSL method based non-parametric Bayesian
clustering.
Semi-supervised clustering methods
Authors: Eric Bair
Date: 2013-07-01 00:00:00
Tasks: Clustering

Cluster analysis methods seek to partition a data set into homogeneous
subgroups. It is useful in a wide variety of applications, including document
processing and modern genetics. Conventional clustering methods are
unsupervised, meaning that there is no outcome variable nor is anything known
about the relationship between the observations in the data set. In many
situations, however, information about the clusters is available in addition to
the values of the features. For example, the cluster labels of some
observations may be known, or certain observations may be known to belong to
the same cluster. In other cases, one may wish to identify clusters that are
associated with a particular outcome variable. This review describes several
clustering algorithms (known as "semi-supervised clustering" methods) that can
be applied in these situations. The majority of these methods are modifications
of the popular k-means clustering method, and several of them will be described
in detail. A brief description of some other semi-supervised clustering
algorithms is also provided.
Sparse Principal Component Analysis for High Dimensional Vector Autoregressive Models
Authors: Zhaoran Wang, Fang Han, Han Liu
Date: 2013-06-30 00:00:00
Tasks: Time Series, Time Series Analysis, Vocal Bursts Intensity Prediction

We study sparse principal component analysis for high dimensional vector
autoregressive time series under a doubly asymptotic framework, which allows
the dimension $d$ to scale with the series length $T$. We treat the transition
matrix of time series as a nuisance parameter and directly apply sparse
principal component analysis on multivariate time series as if the data are
independent. We provide explicit non-asymptotic rates of convergence for
leading eigenvector estimation and extend this result to principal subspace
estimation. Our analysis illustrates that the spectral norm of the transition
matrix plays an essential role in determining the final rates. We also
characterize sufficient conditions under which sparse principal component
analysis attains the optimal parametric rate. Our theoretical results are
backed up by thorough numerical studies.
Hyperspectral Data Unmixing Using GNMF Method and Sparseness Constraint
Authors: Roozbeh Rajabi, Hassan Ghassemian
Date: 2013-06-29 00:00:00

Hyperspectral images contain mixed pixels due to low spatial resolution of
hyperspectral sensors. Mixed pixels are pixels containing more than one
distinct material called endmembers. The presence percentages of endmembers in
mixed pixels are called abundance fractions. Spectral unmixing problem refers
to decomposing these pixels into a set of endmembers and abundance fractions.
Due to nonnegativity constraint on abundance fractions, nonnegative matrix
factorization methods (NMF) have been widely used for solving spectral unmixing
problem. In this paper we have used graph regularized (GNMF) method with
sparseness constraint to unmix hyperspectral data. This method applied on
simulated data using AVIRIS Indian Pines dataset and USGS library and results
are quantified based on AAD and SAD measures. Results in comparison with other
methods show that the proposed method can unmix data more effectively.
Concentration and Confidence for Discrete Bayesian Sequence Predictors
Authors: Tor Lattimore, Marcus Hutter, Peter Sunehag
Date: 2013-06-29 00:00:00

Bayesian sequence prediction is a simple technique for predicting future
symbols sampled from an unknown measure on infinite sequences over a countable
alphabet. While strong bounds on the expected cumulative error are known, there
are only limited results on the distribution of this error. We prove tight
high-probability bounds on the cumulative error, which is measured in terms of
the Kullback-Leibler (KL) divergence. We also consider the problem of
constructing upper confidence bounds on the KL and Hellinger errors similar to
those constructed from Hoeffding-like bounds in the i.i.d. case. The new
results are applied to show that Bayesian sequence prediction can be used in
the Knows What It Knows (KWIK) framework with bounds that match the
state-of-the-art.
Semantics and pragmatics in actual software applications and in web search engines: exploring innovations
Authors: Fabrizio M. A. Lolli
Date: 2013-06-29 00:00:00

While new ways to use the Semantic Web are developed every week, which allow
the user to find information on web more accurately - for example in search
engines - some sophisticated pragmatic tools are becoming more important - for
example in web interfaces known as Social Intelligence, or in the most famous
Siri by Apple. The work aims to analyze whether and where we can identify the
boundary between semantics and pragmatics in the software used by analyzed
systems. examining how the linguistic disciplines are fundamental in their
progress. Is it possible to assume that the tools of social intelligence have a
pragmatic approach to the questions of the user, or it is just a use of a very
rich vocabulary, with the use of semantic tools?
Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs
Authors: Vikash K. Mansinghka, Tejas D. Kulkarni, Yura N. Perov, Joshua B. Tenenbaum
Date: 2013-06-29 00:00:00
Proceeding: NeurIPS 2013 12
Tasks: Probabilistic Programming

The idea of computer vision as the Bayesian inverse problem to computer
graphics has a long history and an appealing elegance, but it has proved
difficult to directly implement. Instead, most vision tasks are approached via
complex bottom-up processing pipelines. Here we show that it is possible to
write short, simple probabilistic graphics programs that define flexible
generative models and to automatically invert them to interpret real-world
images. Generative probabilistic graphics programs consist of a stochastic
scene generator, a renderer based on graphics software, a stochastic likelihood
model linking the renderer's output and the data, and latent variables that
adjust the fidelity of the renderer and the tolerance of the likelihood model.
Representations and algorithms from computer graphics, originally designed to
produce high-quality images, are instead used as the deterministic backbone for
highly approximate and stochastic generative models. This formulation combines
probabilistic programming, computer graphics, and approximate Bayesian
computation, and depends only on general-purpose, automatic inference
techniques. We describe two applications: reading sequences of degraded and
adversarially obscured alphanumeric characters, and inferring 3D road models
from vehicle-mounted camera images. Each of the probabilistic graphics programs
we present relies on under 20 lines of probabilistic code, and supports
accurate, approximately Bayesian inferences about ambiguous real-world images.
P-HGRMS: A Parallel Hypergraph Based Root Mean Square Algorithm for Image Denoising
Authors: Tejaswi Agarwal, Saurabh Jha, B. Rajesh Kanna
Date: 2013-06-23 00:00:00
Tasks: Computational Efficiency, Denoising, Image Denoising

This paper presents a parallel Salt and Pepper (SP) noise removal algorithm
in a grey level digital image based on the Hypergraph Based Root Mean Square
(HGRMS) approach. HGRMS is generic algorithm for identifying noisy pixels in
any digital image using a two level hierarchical serial approach. However, for
SP noise removal, we reduce this algorithm to a parallel model by introducing a
cardinality matrix and an iteration factor, k, which helps us reduce the
dependencies in the existing approach. We also observe that the performance of
the serial implementation is better on smaller images, but once the threshold
is achieved in terms of image resolution, its computational complexity
increases drastically. We test P-HGRMS using standard images from the Berkeley
Segmentation dataset on NVIDIAs Compute Unified Device Architecture (CUDA) for
noise identification and attenuation. We also compare the noise removal
efficiency of the proposed algorithm using Peak Signal to Noise Ratio (PSNR) to
the existing approach. P-HGRMS maintains the noise removal efficiency and
outperforms its sequential counterpart by 6 to 18 times (6x - 18x) in
computational efficiency.
Increasing Compression Ratio in PNG Images by k-Modulus Method for Image Transformation
Authors: Firas A. Jassim
Date: 2013-06-28 00:00:00
Tasks: Image Compression

Image compression is an important filed in image processing. The science
welcomes any tinny contribution that may increase the compression ratio by
whichever insignificant percentage. Therefore, the essential contribution in
this paper is to increase the compression ratio for the well known Portable
Network Graphics (PNG) image file format. The contribution starts with
converting the original PNG image into k-Modulus Method (k-MM). Practically,
taking k equals to ten, and then the pixels in the constructed image will be
integers divisible by ten. Since PNG uses Lempel-Ziv compression algorithm,
then the ability to reduce file size will increase according to the repetition
in pixels in each k-by-k window according to the transformation done by k-MM.
Experimental results show that the proposed technique (k-PNG) produces high
compression ratio with smaller file size in comparison to the original PNG
file.
Memory Limited, Streaming PCA
Authors: Ioannis Mitliagkas, Constantine Caramanis, Prateek Jain
Date: 2013-06-28 00:00:00
Proceeding: NeurIPS 2013 12

We consider streaming, one-pass principal component analysis (PCA), in the
high-dimensional regime, with limited memory. Here, $p$-dimensional samples are
presented sequentially, and the goal is to produce the $k$-dimensional subspace
that best approximates these points. Standard algorithms require $O(p^2)$
memory; meanwhile no algorithm can do better than $O(kp)$ memory, since this is
what the output itself requires. Memory (or storage) complexity is most
meaningful when understood in the context of computational and sample
complexity. Sample complexity for high-dimensional PCA is typically studied in
the setting of the {\em spiked covariance model}, where $p$-dimensional points
are generated from a population covariance equal to the identity (white noise)
plus a low-dimensional perturbation (the spike) which is the signal to be
recovered. It is now well-understood that the spike can be recovered when the
number of samples, $n$, scales proportionally with the dimension, $p$. Yet, all
algorithms that provably achieve this, have memory complexity $O(p^2)$.
Meanwhile, algorithms with memory-complexity $O(kp)$ do not have provable
bounds on sample complexity comparable to $p$. We present an algorithm that
achieves both: it uses $O(kp)$ memory (meaning storage of any kind) and is able
to compute the $k$-dimensional spike with $O(p \log p)$ sample-complexity --
the first algorithm of its kind. While our theoretical analysis focuses on the
spiked covariance model, our simulations show that our algorithm is successful
on much more general models for the data.
Investigation of "Enhancing flexibility and robustness in multi-agent task scheduling"
Authors: Daan Wilmer
Date: 2013-06-28 00:00:00
Tasks: Scheduling

Wilson et al. propose a measure of flexibility in project scheduling problems
and propose several ways of distributing flexibility over tasks without
overrunning the deadline. These schedules prove quite robust: delays of some
tasks do not necessarily lead to delays of subsequent tasks. The number of
tasks that finish late depends, among others, on the way of distributing
flexibility.
  In this paper I study the different flexibility distributions proposed by
Wilson et al. and the differences in number of violations (tasks that finish
too late). I show one factor in the instances that causes differences in the
number of violations, as well as two properties of the flexibility distribution
that cause them to behave differently. Based on these findings, I propose three
new flexibility distributions. Depending on the nature of the delays, these new
flexibility distributions perform as good as or better than the distributions
by Wilson et al.
Bioacoustical Periodic Pulse Train Signal Detection and Classification using Spectrogram Intensity Binarization and Energy Projection
Authors: Marian Popescu, Peter J. Dugan, Mohammad Pourhomayoun, Denise Risch, Harold W. Lewis III, Christopher W. Clark
Date: 2013-05-14 00:00:00
Tasks: Binarization, Edge Detection, General Classification

The following work outlines an approach for automatic detection and
recognition of periodic pulse train signals using a multi-stage process based
on spectrogram edge detection, energy projection and classification. The method
has been implemented to automatically detect and recognize pulse train songs of
minke whales. While the long term goal of this work is to properly identify and
detect minke songs from large multi-year datasets, this effort was developed
using sounds off the coast of Massachusetts, in the Stellwagen Bank National
Marine Sanctuary. The detection methodology is presented and evaluated on 232
continuous hours of acoustic recordings and a qualitative analysis of machine
learning classifiers and their performance is described. The trained automatic
detection and classification system is applied to 120 continuous hours,
comprised of various challenges such as broadband and narrowband noises, low
SNR, and other pulse train signatures. This automatic system achieves a TPR of
63% for FPR of 0.6% (or 0.87 FP/h), at a Precision (PPV) of 84% and an F1 score
of 71%.
Dynamic Decision Making for Graphical Models Applied to Oil Exploration
Authors: Gabriele Martinelli, Jo Eidsvik, Ragnar Hauge
Date: 2012-01-20 00:00:00
Tasks: Decision Making, Sequential Decision Making

This paper has been withdrawn by the authors. We present a framework for
sequential decision making in problems described by graphical models. The
setting is given by dependent discrete random variables with associated costs
or revenues. In our examples, the dependent variables are the potential
outcomes (oil, gas or dry) when drilling a petroleum well. The goal is to
develop an optimal selection strategy that incorporates a chosen utility
function within an approximated dynamic programming scheme. We propose and
compare different approximations, from simple heuristics to more complex
iterative schemes, and we discuss their computational properties. We apply our
strategies to oil exploration over multiple prospects modeled by a directed
acyclic graph, and to a reservoir drilling decision problem modeled by a Markov
random field. The results show that the suggested strategies clearly improve
the simpler intuitive constructions, and this is useful when selecting
exploration policies.
Axiomatic properties of inconsistency indices for pairwise comparisons
Authors: Matteo Brunelli, Michele Fedrizzi
Date: 2013-06-28 00:00:00
Tasks: Decision Making

Pairwise comparisons are a well-known method for the representation of the
subjective preferences of a decision maker. Evaluating their inconsistency has
been a widely studied and discussed topic and several indices have been
proposed in the literature to perform this task. Since an acceptable level of
consistency is closely related with the reliability of preferences, a suitable
choice of an inconsistency index is a crucial phase in decision making
processes. The use of different methods for measuring consistency must be
carefully evaluated, as it can affect the decision outcome in practical
applications. In this paper, we present five axioms aimed at characterizing
inconsistency indices. In addition, we prove that some of the indices proposed
in the literature satisfy these axioms, while others do not, and therefore, in
our view, they may fail to correctly evaluate inconsistency.
New Mathematical and Algorithmic Schemes for Pattern Classification with Application to the Identification of Writers of Important Ancient Documents
Authors: Dimitris Arabadjis, Fotios Giannopoulos, Constantin Papaodysseus, Solomon Zannos, Panayiotis Rousopoulos, Michail Panagopoulos, Christopher Blackwell
Date: 2013-06-28 00:00:00
Tasks: General Classification

In this paper, a novel approach is introduced for classifying curves into
proper families, according to their similarity. First, a mathematical quantity
we call plane curvature is introduced and a number of propositions are stated
and proved. Proper similarity measures of two curves are introduced and a
subsequent statistical analysis is applied. First, the efficiency of the curve
fitting process has been tested on 2 shapes datasets of reference. Next, the
methodology has been applied to the very important problem of classifying 23
Byzantine codices and 46 Ancient inscriptions to their writers, thus achieving
correct dating of their content. The inscriptions have been attributed to ten
individual hands and the Byzantine codices to four writers.
ABC Reinforcement Learning
Authors: Christos Dimitrakakis, Nikolaos Tziortziotis
Date: 2013-03-27 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper introduces a simple, general framework for likelihood-free
Bayesian reinforcement learning, through Approximate Bayesian Computation
(ABC). The main advantage is that we only require a prior distribution on a
class of simulators (generative models). This is useful in domains where an
analytical probabilistic model of the underlying process is too complex to
formulate, but where detailed simulation models are available. ABC-RL allows
the use of any Bayesian reinforcement learning technique, even in this case. In
addition, it can be seen as an extension of rollout algorithms to the case
where we do not know what the correct model to draw rollouts from is. We
experimentally demonstrate the potential of this approach in a comparison with
LSPI. Finally, we introduce a theorem showing that ABC is a sound methodology
in principle, even when non-sufficient statistics are used.