99,979 research outputs found
Effective Discriminative Feature Selection with Non-trivial Solutions
Feature selection and feature transformation, the two main ways to reduce
dimensionality, are often presented separately. In this paper, a feature
selection method is proposed by combining the popular transformation based
dimensionality reduction method Linear Discriminant Analysis (LDA) and sparsity
regularization. We impose row sparsity on the transformation matrix of LDA
through -norm regularization to achieve feature selection, and
the resultant formulation optimizes for selecting the most discriminative
features and removing the redundant ones simultaneously. The formulation is
extended to the -norm regularized case: which is more likely to
offer better sparsity when . Thus the formulation is a better
approximation to the feature selection problem. An efficient algorithm is
developed to solve the -norm based optimization problem and it is
proved that the algorithm converges when . Systematical experiments
are conducted to understand the work of the proposed method. Promising
experimental results on various types of real-world data sets demonstrate the
effectiveness of our algorithm
Supporting the reconciliation of models of object behaviour
This paper presents Reconciliation+, a method which identifies overlaps between models of software systems behaviour expressed as UML object interaction diagrams (i.e., sequence and/or collaboration diagrams), checks whether the overlapping elements of these models satisfy specific consistency rules and, in cases where they violate these rules, guides software designers in handling the detected inconsistencies. The method detects overlaps between object interaction diagrams by using a probabilistic message matching algorithm that has been developed for this purpose. The guidance to software designers on when to check for inconsistencies and how to deal with them is delivered by enacting a built-in process model that specifies the consistency rules that can be checked against overlapping models and different ways of handling violations of these rules. Reconciliation+ is supported by a toolkit. It has also been evaluated in a case study. This case study has produced positive results which are discussed in the paper
Discriminative variable selection for clustering with the sparse Fisher-EM algorithm
The interest in variable selection for clustering has increased recently due
to the growing need in clustering high-dimensional data. Variable selection
allows in particular to ease both the clustering and the interpretation of the
results. Existing approaches have demonstrated the efficiency of variable
selection for clustering but turn out to be either very time consuming or not
sparse enough in high-dimensional spaces. This work proposes to perform a
selection of the discriminative variables by introducing sparsity in the
loading matrix of the Fisher-EM algorithm. This clustering method has been
recently proposed for the simultaneous visualization and clustering of
high-dimensional data. It is based on a latent mixture model which fits the
data into a low-dimensional discriminative subspace. Three different approaches
are proposed in this work to introduce sparsity in the orientation matrix of
the discriminative subspace through -type penalizations. Experimental
comparisons with existing approaches on simulated and real-world data sets
demonstrate the interest of the proposed methodology. An application to the
segmentation of hyperspectral images of the planet Mars is also presented
An Effective Feature Selection Method Based on Pair-Wise Feature Proximity for High Dimensional Low Sample Size Data
Feature selection has been studied widely in the literature. However, the
efficacy of the selection criteria for low sample size applications is
neglected in most cases. Most of the existing feature selection criteria are
based on the sample similarity. However, the distance measures become
insignificant for high dimensional low sample size (HDLSS) data. Moreover, the
variance of a feature with a few samples is pointless unless it represents the
data distribution efficiently. Instead of looking at the samples in groups, we
evaluate their efficiency based on pairwise fashion. In our investigation, we
noticed that considering a pair of samples at a time and selecting the features
that bring them closer or put them far away is a better choice for feature
selection. Experimental results on benchmark data sets demonstrate the
effectiveness of the proposed method with low sample size, which outperforms
many other state-of-the-art feature selection methods.Comment: European Signal Processing Conference 201
Learning Discriminative Stein Kernel for SPD Matrices and Its Applications
Stein kernel has recently shown promising performance on classifying images
represented by symmetric positive definite (SPD) matrices. It evaluates the
similarity between two SPD matrices through their eigenvalues. In this paper,
we argue that directly using the original eigenvalues may be problematic
because: i) Eigenvalue estimation becomes biased when the number of samples is
inadequate, which may lead to unreliable kernel evaluation; ii) More
importantly, eigenvalues only reflect the property of an individual SPD matrix.
They are not necessarily optimal for computing Stein kernel when the goal is to
discriminate different sets of SPD matrices. To address the two issues in one
shot, we propose a discriminative Stein kernel, in which an extra parameter
vector is defined to adjust the eigenvalues of the input SPD matrices. The
optimal parameter values are sought by optimizing a proxy of classification
performance. To show the generality of the proposed method, three different
kernel learning criteria that are commonly used in the literature are employed
respectively as a proxy. A comprehensive experimental study is conducted on a
variety of image classification tasks to compare our proposed discriminative
Stein kernel with the original Stein kernel and other commonly used methods for
evaluating the similarity between SPD matrices. The experimental results
demonstrate that, the discriminative Stein kernel can attain greater
discrimination and better align with classification tasks by altering the
eigenvalues. This makes it produce higher classification performance than the
original Stein kernel and other commonly used methods.Comment: 13 page
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