Dictionary construction for patch-to-tensor embedding

Moshe Salhov, Guy Wolf, Amit Bermanis, Amir Averbuch, Pekka Neittaanmäki

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

Abstract

The incorporation of matrix relation, which can encompass multidimensional similarities between local neighborhoods of points in the manifold, can improve kernel based data analysis. However, the utilization of multidimensional similarities results in a larger kernel and hence the computational cost of the corresponding spectral decomposition increases dramatically. In this paper, we propose dictionary construction to approximate the kernel in this case and its respected embedding. The proposed dictionary construction is demonstrated on a relevant example of a super kernel that is based on the utilization of the diffusion maps kernel together with linear-projection operators between tangent spaces of the manifold.

Original languageEnglish
Title of host publicationAdvances in Intelligent Data Analysis XI - 11th International Symposium, IDA 2012, Proceedings
Pages346-356
Number of pages11
DOIs
StatePublished - 2012
Event11th International Symposium on Intelligent Data Analysis, IDA 2012 - Helsinki, Finland
Duration: 25 Oct 201227 Oct 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7619 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th International Symposium on Intelligent Data Analysis, IDA 2012
Country/TerritoryFinland
CityHelsinki
Period25/10/1227/10/12

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