Efficiently Downdating, Composing and Splitting Singular Value Decompositions Preserving the Mean Information

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Títol:	Efficiently Downdating, Composing and Splitting Singular Value Decompositions Preserving the Mean Information
Autoria:	Melenchón, Javier Martínez, Elisa
Citació:	Melenchón, J. [Javier], Martínez, E. [Elisa]. (2007). Efficiently Downdating, Composing and Splitting Singular Value Decompositions Preserving the Mean Information. Pattern Recognition and Image Analysis (IbPRIA 2007), 4478. doi: 10.1007/978-3-540-72849-8_55
Resum:	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.
Paraules clau:	video sequence singular value decomposition high dimensional data singular vector latent semantic analysis
DOI:	https://doi.org/10.1007/978-3-540-72849-8_55
Tipus de document:	info:eu-repo/semantics/conferenceObject
Versió del document:	info:eu-repo/semantics/publishedVersion
Data de publicació:	1-jul-2007
Llicència de publicació:	https://creativecommons.org/licenses/by/4.0/
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