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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Discriminative Dimensionality Reduction Mappings
verfasst von : Andrej Gisbrecht, Daniela Hofmann, Barbara Hammer
Erschienen in: Advances in Intelligent Data Analysis XI
Verlag: Springer Berlin Heidelberg
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Discriminative dimensionality reduction aims at a low dimensional, usually nonlinear representation of given data such that information as specified by auxiliary discriminative labeling is presented as accurately as possible. This paper centers around two open problems connected to this question: (i) how to evaluate discriminative dimensionality reduction quantitatively? (ii) how to arrive at explicit nonlinear discriminative dimensionality reduction mappings? Based on recent work for the unsupervised case, we propose an evaluation measure and an explicit discriminative dimensionality reduction mapping using the Fisher information.