Dimensionality Reduction Mappings

Kerstin Bunte, Michael Biehl, Barbara Hammer

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

7 Citations (Scopus)
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Abstract

A wealth of powerful dimensionality reduction methods has been established which can be used for data visualization and preprocessing. These are accompanied by formal evaluation schemes, which allow a quantitative evaluation along general principles and which even lead to further visualization schemes based on these objectives. Most methods, however, provide a mapping of a priorly given finite set of points only, requiring additional steps for out-of-sample extensions. We propose a general view on dimensionality reduction based on the concept of cost functions, and, based on this general principle, extend dimensionality reduction to explicit mappings of the data manifold. This offers simple out-of-sample extensions. Further, it opens a way towards a theory of data visualization taking the perspective of its generalization ability to new data points. We demonstrate the approach based on a simple global linear mapping as well as prototype-based local linear mappings.
Original languageEnglish
Title of host publicationProc. IEEE Symp. on Computational Intelligence and Data Mining SSCI 2011 CDIM
PublisherIEEE (The Institute of Electrical and Electronics Engineers)
Pages349-356
Number of pages8
ISBN (Print)978-1-4244-9926-7
DOIs
Publication statusPublished - 2011

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