By the same authors

A Riemannian Self-Organizing Map

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Publication details

Title of host publicationIMAGE ANALYSIS AND PROCESSING - ICIAP 2009, PROCEEDINGS
DatePublished - 2009
Pages229-238
Number of pages10
PublisherSPRINGER-VERLAG BERLIN
Place of PublicationBERLIN
EditorsP Foggia, C Sansone, M Vento
Volume5716 LNCS
Original languageEnglish
ISBN (Print)978-3-642-04145-7

Abstract

We generalize the classic self-organizing limp (SOM) in Hat Euclidean space (linear manifold) onto a Riemannian Both sequential and hatch learning algorithms for the generalized SOM are presented. Compared with the classical SOM; the most novel feature of the generalized SOM is that it can learn the intrinsic topological neighborhood structure of the underlying Riemannian manifold that fits to the input data. We here compared the performance of the generalized SOM and the classical SOM using real 3-Dimensional facial surface normals data. Experimental results show that the generalized SOM outperforms the classical SOM when the data lie on a curvet Riemannian manifold.

Bibliographical note

15th International Conference on Image Analysis and Processing (ICIAP 2009), Vietri sul Mare, ITALY, SEP 08-11, 2009

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