The test suite optimization during test case generation can save time and cost. The paper presents an information theory based metric to filter the redundant test cases and reduce the test suite size while, maintaining the coverage of the requirements and with minimum loss to mutant coverage. The paper propose two versions, RR and RR2. RR filters test cases for each requirement, where as, RR2 f...

Information visualization can be considered a process of transforming similarity relationships between data points to a geometric representation in order to see unseen information. High-dimensionality data sets are one of the main problems of information visualization. Dimensionality Reduction (DR) is therefore a useful strategy to project high-dimensional space onto low-dimensional space, whic...

This report concerns the problem of dimensionality reduction through information geometric methods on statistical manifolds. While there has been considerable work recently presented regarding dimensionality reduction for the purposes of learning tasks such as classification, clustering, and visualization, these methods have focused primarily on Riemannian manifolds in Euclidean space. While su...

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To make a supervisor comprehensible to a layman has been a long-lasting goal in the supervisory control community. One strategy is to reduce the size of a supervisor to generate a control equivalent version, whose size is hopefully much smaller than the original one so that a user or control designer can easily check whether a designed controller fulfils its objectives and requirements. After t...

If language users are rational, they might choose to structure their utterances so as to optimize communicative properties. In particular, information-theoretic and psycholinguistic considerations suggest that this may include maximizing the uniformity of information density in an utterance. We investigate this possibility in the context of syntactic reduction, where the speaker has the option ...

During the past decades, to study high-dimensional data in a large variety of problems, researchers have proposed many Feature Extraction algorithms. One of the most effective approaches for optimal feature extraction is based on mutual information (MI). However it is not always easy to get an accurate estimation for high dimensional MI. In terms of MI, the optimal feature extraction is creatin...

In this thesis, I look at three problems with important applications in data processing. Incorporating side information, provided by the user or derived from data, is a main theme of each of these problems. This thesis makes a number of contributions. The first is a technique for combining different embedding objectives, which is then exploited to incorporate side information expressed in terms...

In the presence of pronunciation variation and the masking effects of additive noise, we investigate the role of phonetic information reduction and lexical confusability on ASR performance. Contrary to previous work [1], we show that place of articulation as a representation for unstressed segments performs at least as well as manner of articulation in the presence of additive noise. Methods of...

The basic idea of linear Principal Component Analyses (PCA) consists in decorrelating coordinates by an orthogonal linear transformation. In this paper we generalize this idea to the nonlinear case. Simultaneously we will drop the usual restriction to gaussian distributions. The linearity and orthogonality condition of linear PCA is substituted with the condition of volume conservation in order...