Efficiently downdating, composing and splitting singular value decompositions preserving the mean information

Javier Melenchón, Elisa Martínez

Research output: A Conference proceeding or a Chapter in BookConference contribution

5 Citations (Scopus)

Abstract

Three methods for the efficient downdating, composition and splitting of low rank singular value decompositions are proposed. They are formulated in a closed form, considering the mean information and providing exact results. Although these methods are presented in the context of computer vision, they can be used in any field forgetting information, combining different eigenspaces in one or ignoring particular dimensions of the column space of the data. Application examples on face subspace learning and latent semantic analysis are given and performance results are provided.

Original languageEnglish
Title of host publicationPattern Recognition and Image Analysis - Third Iberian Conference, IbPRIA 2007, Proceedings
Subtitle of host publicationLecture Notes in Computer Science
Place of PublicationGirona, Spain
PublisherSpringer
Pages436-443
Number of pages8
Volume4478
EditionPART 2
ISBN (Print)9783540728481
DOIs
Publication statusPublished - 6 Jun 2007
Externally publishedYes
Event3rd Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2007 - Girona, Spain
Duration: 6 Jun 20078 Jun 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume4478 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2007
CountrySpain
CityGirona
Period6/06/078/06/07

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