pwc-archive/papers-with-abstracts huggingface dataset:
Solving Traveling Salesman Problem by Marker Method
Authors: Masoumeh Vali
Date: 2013-07-22 00:00:00
Tasks: Traveling Salesman Problem

In this paper we use marker method and propose a new mutation operator that
selects the nearest neighbor among all near neighbors solving Traveling
Salesman Problem.
Rotational Mutation Genetic Algorithm on optimization Problems
Authors: Masoumeh Vali
Date: 2013-07-22 00:00:00

Optimization problem, nowadays, have more application in all major but they
have problem in computation. Calculation of the optimum point in the spaces
with the above dimensions is very time consuming. In this paper, there is
presented a new approach for the optimization of continuous functions with
rotational mutation that is called RM. The proposed algorithm starts from the
point which has best fitness value by elitism mechanism. Then, method of
rotational mutation is used to reach optimal point. In this paper, RM algorithm
is implemented by GA(Briefly RMGA) and is compared with other well- known
algorithms: DE, PGA, Grefensstette and Eshelman [15, 16] and numerical and
simulation results show that RMGA achieve global optimal point with more
decision by smaller generations.
New Optimization Approach Using Clustering-Based Parallel Genetic Algorithm
Authors: Masoumeh Vali
Date: 2013-07-22 00:00:00
Tasks: Clustering, global-optimization

In many global Optimization Problems, it is required to evaluate a global
point (min or max) in large space that calculation effort is very high. In this
paper is presented new approach for optimization problem with subdivision
labeling method (SLM) but in this method for higher dimensional has high
calculation effort. Clustering-Based Parallel Genetic Algorithm (CBPGA) in
optimization problems is one of the solutions of this problem. That the initial
population is crossing points and subdividing in each step is according to
mutation. After labeling all of crossing points, selecting is according to
polytope that has complete label. In this method we propose an algorithm, based
on parallelization scheme using master-slave. SLM algorithm is implemented by
CBPGA and compared the experimental results. The numerical examples and
numerical results show that SLMCBPGA is improved speed up and efficiency.
Online Tracking Parameter Adaptation based on Evaluation
Authors: Duc Phu Chau, Julien Badie, François Bremond, Monique Thonnat
Date: 2013-07-22 00:00:00

Parameter tuning is a common issue for many tracking algorithms. In order to
solve this problem, this paper proposes an online parameter tuning to adapt a
tracking algorithm to various scene contexts. In an offline training phase,
this approach learns how to tune the tracker parameters to cope with different
contexts. In the online control phase, once the tracking quality is evaluated
as not good enough, the proposed approach computes the current context and
tunes the tracking parameters using the learned values. The experimental
results show that the proposed approach improves the performance of the
tracking algorithm and outperforms recent state of the art trackers. This paper
brings two contributions: (1) an online tracking evaluation, and (2) a method
to adapt online tracking parameters to scene contexts.
Unsupervised model-free representation learning
Authors: Daniil Ryabko
Date: 2013-04-17 00:00:00
Tasks: model, Representation Learning, Time Series, Time Series Analysis

Numerous control and learning problems face the situation where sequences of high-dimensional highly dependent data are available but no or little feedback is provided to the learner, which makes any inference rather challenging. To address this challenge, we formulate the following problem. Given a series of observations $X_0,\dots,X_n$ coming from a large (high-dimensional) space $\mathcal X$, find a representation function $f$ mapping $\mathcal X$ to a finite space $\mathcal Y$ such that the series $f(X_0),\dots,f(X_n)$ preserves as much information as possible about the original time-series dependence in $X_0,\dots,X_n$. We show that, for stationary time series, the function $f$ can be selected as the one maximizing a certain information criterion that we call time-series information. Some properties of this functions are investigated, including its uniqueness and consistency of its empirical estimates. Implications for the problem of optimal control are presented.
Multi-horizon solar radiation forecasting for Mediterranean locations using time series models
Authors: Cyril Voyant, Christophe Paoli, Marc Muselli, Marie Laure Nivet
Date: 2013-07-22 00:00:00
Tasks: Prediction, Time Series, Time Series Analysis

Considering the grid manager's point of view, needs in terms of prediction of
intermittent energy like the photovoltaic resource can be distinguished
according to the considered horizon: following days (d+1, d+2 and d+3), next
day by hourly step (h+24), next hour (h+1) and next few minutes (m+5 e.g.).
Through this work, we have identified methodologies using time series models
for the prediction horizon of global radiation and photovoltaic power. What we
present here is a comparison of different predictors developed and tested to
propose a hierarchy. For horizons d+1 and h+1, without advanced ad hoc time
series pre-processing (stationarity) we find it is not easy to differentiate
between autoregressive moving average (ARMA) and multilayer perceptron (MLP).
However we observed that using exogenous variables improves significantly the
results for MLP . We have shown that the MLP were more adapted for horizons
h+24 and m+5. In summary, our results are complementary and improve the
existing prediction techniques with innovative tools: stationarity, numerical
weather prediction combination, MLP and ARMA hybridization, multivariate
analysis, time index, etc.
Performance comparison of State-of-the-art Missing Value Imputation Algorithms on Some Bench mark Datasets
Authors: M. Naresh Kumar
Date: 2013-07-22 00:00:00
Tasks: Decision Making, General Classification, Imputation, Missing Values

Decision making from data involves identifying a set of attributes that
contribute to effective decision making through computational intelligence. The
presence of missing values greatly influences the selection of right set of
attributes and this renders degradation in classification accuracies of the
classifiers. As missing values are quite common in data collection phase during
field experiments or clinical trails appropriate handling would improve the
classifier performance. In this paper we present a review of recently developed
missing value imputation algorithms and compare their performance on some bench
mark datasets.
A Novel Equation based Classifier for Detecting Human in Images
Authors: Subra Mukherjee, Karen Das
Date: 2013-07-22 00:00:00
Tasks: Object Recognition

Shape based classification is one of the most challenging tasks in the field
of computer vision. Shapes play a vital role in object recognition. The basic
shapes in an image can occur in varying scale, position and orientation. And
specially when detecting human, the task becomes more challenging owing to the
largely varying size, shape, posture and clothing of human. So, in our work we
detect human, based on the head-shoulder shape as it is the most unvarying part
of human body. Here, firstly a new and a novel equation named as the Omega
Equation that describes the shape of human head-shoulder is developed and based
on this equation, a classifier is designed particularly for detecting human
presence in a scene. The classifier detects human by analyzing some of the
discriminative features of the values of the parameters obtained from the Omega
equation. The proposed method has been tested on a variety of shape dataset
taking into consideration the complexities of human head-shoulder shape. In all
the experiments the proposed method demonstrated satisfactory results.
An Information Theoretic Measure of Judea Pearl's Identifiability and Causal Influence
Authors: Robert R. Tucci
Date: 2013-07-21 00:00:00

In this paper, we define a new information theoretic measure that we call the
"uprooted information". We show that a necessary and sufficient condition for a
probability $P(s|do(t))$ to be "identifiable" (in the sense of Pearl) in a
graph $G$ is that its uprooted information be non-negative for all models of
the graph $G$. In this paper, we also give a new algorithm for deciding, for a
Bayesian net that is semi-Markovian, whether a probability $P(s|do(t))$ is
identifiable, and, if it is identifiable, for expressing it without allusions
to confounding variables. Our algorithm is closely based on a previous
algorithm by Tian and Pearl, but seems to correct a small flaw in theirs. In
this paper, we also find a {\it necessary and sufficient graphical condition}
for a probability $P(s|do(t))$ to be identifiable when $t$ is a singleton set.
So far, in the prior literature, it appears that only a {\it sufficient
graphical condition} has been given for this. By "graphical" we mean that it is
directly based on Judea Pearl's 3 rules of do-calculus.
Regularized Discrete Optimal Transport
Authors: Sira Ferradans, Nicolas Papadakis, Gabriel Peyré, Jean-François Aujol
Date: 2013-07-21 00:00:00
Tasks: Colorization, Color Normalization

This article introduces a generalization of the discrete optimal transport,
with applications to color image manipulations. This new formulation includes a
relaxation of the mass conservation constraint and a regularization term. These
two features are crucial for image processing tasks, which necessitate to take
into account families of multimodal histograms, with large mass variation
across modes.
  The corresponding relaxed and regularized transportation problem is the
solution of a convex optimization problem. Depending on the regularization
used, this minimization can be solved using standard linear programming methods
or first order proximal splitting schemes.
  The resulting transportation plan can be used as a color transfer map, which
is robust to mass variation across images color palettes. Furthermore, the
regularization of the transport plan helps to remove colorization artifacts due
to noise amplification.
  We also extend this framework to the computation of barycenters of
distributions. The barycenter is the solution of an optimization problem, which
is separately convex with respect to the barycenter and the transportation
plans, but not jointly convex. A block coordinate descent scheme converges to a
stationary point of the energy. We show that the resulting algorithm can be
used for color normalization across several images. The relaxed and regularized
barycenter defines a common color palette for those images. Applying color
transfer toward this average palette performs a color normalization of the
input images.
A New Optimization Approach Based on Rotational Mutation and Crossover Operator
Authors: Masoumeh Vali
Date: 2013-07-21 00:00:00
Tasks: global-optimization

Evaluating a global optimal point in many global optimization problems in
large space is required to more calculations. In this paper, there is presented
a new approach for the continuous functions optimization with rotational
mutation and crossover operator. This proposed method (RMC) starts from the
point which has best fitness value by elitism mechanism and after that
rotational mutation and crossover operator are used to reach optimal point. RMC
method is implemented by GA (Briefly RMCGA) and is compared with other
wellknown algorithms such as: DE, PGA, Grefensstette and Eshelman[15,16] and
numerical and simulating results show that RMCGA achieve global optimal point
with more decision by smaller generations.
A Massively Parallel Associative Memory Based on Sparse Neural Networks
Authors: Zhe Yao, Vincent Gripon, Michael G. Rabbat
Date: 2013-03-28 00:00:00
Tasks: Anomaly Detection, Diversity, Face Recognition, GPU, Retrieval

Associative memories store content in such a way that the content can be
later retrieved by presenting the memory with a small portion of the content,
rather than presenting the memory with an address as in more traditional
memories. Associative memories are used as building blocks for algorithms
within database engines, anomaly detection systems, compression algorithms, and
face recognition systems. A classical example of an associative memory is the
Hopfield neural network. Recently, Gripon and Berrou have introduced an
alternative construction which builds on ideas from the theory of error
correcting codes and which greatly outperforms the Hopfield network in
capacity, diversity, and efficiency. In this paper we implement a variation of
the Gripon-Berrou associative memory on a general purpose graphical processing
unit (GPU). The work of Gripon and Berrou proposes two retrieval rules,
sum-of-sum and sum-of-max. The sum-of-sum rule uses only matrix-vector
multiplication and is easily implemented on the GPU. The sum-of-max rule is
much less straightforward to implement because it involves non-linear
operations. However, the sum-of-max rule gives significantly better retrieval
error rates. We propose a hybrid rule tailored for implementation on a GPU
which achieves a 880-fold speedup without sacrificing any accuracy.
Combining One-Class Classifiers via Meta-Learning
Authors: Eitan Menahem, Lior Rokach, Yuval Elovici
Date: 2011-12-22 00:00:00
Tasks: General Classification, Meta-Learning, One-Class Classification

Selecting the best classifier among the available ones is a difficult task,
especially when only instances of one class exist. In this work we examine the
notion of combining one-class classifiers as an alternative for selecting the
best classifier. In particular, we propose two new one-class classification
performance measures to weigh classifiers and show that a simple ensemble that
implements these measures can outperform the most popular one-class ensembles.
Furthermore, we propose a new one-class ensemble scheme, TUPSO, which uses
meta-learning to combine one-class classifiers. Our experiments demonstrate the
superiority of TUPSO over all other tested ensembles and show that the TUPSO
performance is statistically indistinguishable from that of the hypothetical
best classifier.
Optimal Recombination in Genetic Algorithms
Authors: Anton V. Eremeev, Julia V. Kovalenko
Date: 2013-07-21 00:00:00

This paper surveys results on complexity of the optimal recombination problem
(ORP), which consists in finding the best possible offspring as a result of a
recombination operator in a genetic algorithm, given two parent solutions. We
consider efficient reductions of the ORPs, allowing to establish polynomial
solvability or NP-hardness of the ORPs, as well as direct proofs of hardness
results.
A scalable stage-wise approach to large-margin multi-class loss based boosting
Authors: Sakrapee Paisitkriangkrai, Chunhua Shen, Anton Van Den Hengel
Date: 2013-07-21 00:00:00
Tasks: Classification, General Classification, Multi-class Classification

We present a scalable and effective classification model to train multi-class
boosting for multi-class classification problems. Shen and Hao introduced a
direct formulation of multi- class boosting in the sense that it directly
maximizes the multi- class margin [C. Shen and Z. Hao, "A direct formulation
for totally-corrective multi- class boosting", in Proc. IEEE Conf. Comp. Vis.
Patt. Recogn., 2011]. The major problem of their approach is its high
computational complexity for training, which hampers its application on
real-world problems. In this work, we propose a scalable and simple stage-wise
multi-class boosting method, which also directly maximizes the multi-class
margin. Our approach of- fers a few advantages: 1) it is simple and
computationally efficient to train. The approach can speed up the training time
by more than two orders of magnitude without sacrificing the classification
accuracy. 2) Like traditional AdaBoost, it is less sensitive to the choice of
parameters and empirically demonstrates excellent generalization performance.
Experimental results on challenging multi-class machine learning and vision
tasks demonstrate that the proposed approach substantially improves the
convergence rate and accuracy of the final visual detector at no additional
computational cost compared to existing multi-class boosting.
On GROUSE and Incremental SVD
Authors: Laura Balzano, Stephen J. Wright
Date: 2013-07-21 00:00:00

GROUSE (Grassmannian Rank-One Update Subspace Estimation) is an incremental
algorithm for identifying a subspace of Rn from a sequence of vectors in this
subspace, where only a subset of components of each vector is revealed at each
iteration. Recent analysis has shown that GROUSE converges locally at an
expected linear rate, under certain assumptions. GROUSE has a similar flavor to
the incremental singular value decomposition algorithm, which updates the SVD
of a matrix following addition of a single column. In this paper, we modify the
incremental SVD approach to handle missing data, and demonstrate that this
modified approach is equivalent to GROUSE, for a certain choice of an
algorithmic parameter.
Dictionary LASSO: Guaranteed Sparse Recovery under Linear Transformation
Authors: Ji Liu, Lei Yuan, Jieping Ye
Date: 2013-04-30 00:00:00

We consider the following signal recovery problem: given a measurement matrix
$\Phi\in \mathbb{R}^{n\times p}$ and a noisy observation vector $c\in
\mathbb{R}^{n}$ constructed from $c = \Phi\theta^* + \epsilon$ where
$\epsilon\in \mathbb{R}^{n}$ is the noise vector whose entries follow i.i.d.
centered sub-Gaussian distribution, how to recover the signal $\theta^*$ if
$D\theta^*$ is sparse {\rca under a linear transformation}
$D\in\mathbb{R}^{m\times p}$? One natural method using convex optimization is
to solve the following problem: $$\min_{\theta} {1\over 2}\|\Phi\theta - c\|^2
+ \lambda\|D\theta\|_1.$$ This paper provides an upper bound of the estimate
error and shows the consistency property of this method by assuming that the
design matrix $\Phi$ is a Gaussian random matrix. Specifically, we show 1) in
the noiseless case, if the condition number of $D$ is bounded and the
measurement number $n\geq \Omega(s\log(p))$ where $s$ is the sparsity number,
then the true solution can be recovered with high probability; and 2) in the
noisy case, if the condition number of $D$ is bounded and the measurement
increases faster than $s\log(p)$, that is, $s\log(p)=o(n)$, the estimate error
converges to zero with probability 1 when $p$ and $s$ go to infinity. Our
results are consistent with those for the special case $D=\bold{I}_{p\times p}$
(equivalently LASSO) and improve the existing analysis. The condition number of
$D$ plays a critical role in our analysis. We consider the condition numbers in
two cases including the fused LASSO and the random graph: the condition number
in the fused LASSO case is bounded by a constant, while the condition number in
the random graph case is bounded with high probability if $m\over p$ (i.e.,
$#text{edge}\over #text{vertex}$) is larger than a certain constant. Numerical
simulations are consistent with our theoretical results.
Clustering Algorithm for Gujarati Language
Authors: Miral Patel, Prem Balani
Date: 2013-07-20 00:00:00
Tasks: Clustering

Natural language processing area is still under research. But now a day it is
on platform for worldwide researchers. Natural language processing includes
analyzing the language based on its structure and then tagging of each word
appropriately with its grammar base. Here we have 50,000 tagged words set and
we try to cluster those Gujarati words based on proposed algorithm, we have
defined our own algorithm for processing. Many clustering techniques are
available Ex. Single linkage, complete, linkage,average linkage, Hear no of
clusters to be formed are not known, so it is all depends on the type of data
set provided . Clustering is preprocess for stemming . Stemming is the process
where root is extracted from its word. Ex. cats= cat+S, meaning. Cat: Noun and
plural form.
Tensor-based formulation and nuclear norm regularization for multi-energy computed tomography
Authors: Oguz Semerci, Ning Hao, Misha E. Kilmer, Eric L. Miller
Date: 2013-07-19 00:00:00
Tasks: Computed Tomography (CT)

The development of energy selective, photon counting X-ray detectors allows
for a wide range of new possibilities in the area of computed tomographic image
formation. Under the assumption of perfect energy resolution, here we propose a
tensor-based iterative algorithm that simultaneously reconstructs the X-ray
attenuation distribution for each energy. We use a multi-linear image model
rather than a more standard "stacked vector" representation in order to develop
novel tensor-based regularizers. Specifically, we model the multi-spectral
unknown as a 3-way tensor where the first two dimensions are space and the
third dimension is energy. This approach allows for the design of tensor
nuclear norm regularizers, which like its two dimensional counterpart, is a
convex function of the multi-spectral unknown. The solution to the resulting
convex optimization problem is obtained using an alternating direction method
of multipliers (ADMM) approach. Simulation results shows that the generalized
tensor nuclear norm can be used as a stand alone regularization technique for
the energy selective (spectral) computed tomography (CT) problem and when
combined with total variation regularization it enhances the regularization
capabilities especially at low energy images where the effects of noise are
most prominent.
The Cluster Graphical Lasso for improved estimation of Gaussian graphical models
Authors: Kean Ming Tan, Daniela Witten, Ali Shojaie
Date: 2013-07-19 00:00:00
Tasks: Clustering, Model Selection

We consider the task of estimating a Gaussian graphical model in the
high-dimensional setting. The graphical lasso, which involves maximizing the
Gaussian log likelihood subject to an l1 penalty, is a well-studied approach
for this task. We begin by introducing a surprising connection between the
graphical lasso and hierarchical clustering: the graphical lasso in effect
performs a two-step procedure, in which (1) single linkage hierarchical
clustering is performed on the variables in order to identify connected
components, and then (2) an l1-penalized log likelihood is maximized on the
subset of variables within each connected component. In other words, the
graphical lasso determines the connected components of the estimated network
via single linkage clustering. Unfortunately, single linkage clustering is
known to perform poorly in certain settings. Therefore, we propose the cluster
graphical lasso, which involves clustering the features using an alternative to
single linkage clustering, and then performing the graphical lasso on the
subset of variables within each cluster. We establish model selection
consistency for this technique, and demonstrate its improved performance
relative to the graphical lasso in a simulation study, as well as in
applications to an equities data set, a university webpage data set, and a gene
expression data set.