Probabilistic Latent Variable Models as Nonnegative Factorizations

This paper presents a family of probabilistic latent variable models that can be used for analysis of nonnegative data. We show that there are strong ties between nonnegative matrix factorization and this family, and provide some straightforward extensions which can help in dealing with shift invari...

Descripción completa

Detalles Bibliográficos
Autores principales: Shashanka, Madhusudana, Raj, Bhiksha, Smaragdis, Paris
Formato: Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2008
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2391276/
https://www.ncbi.nlm.nih.gov/pubmed/18509481
http://dx.doi.org/10.1155/2008/947438
_version_ 1782155359439290368
author Shashanka, Madhusudana
Raj, Bhiksha
Smaragdis, Paris
author_facet Shashanka, Madhusudana
Raj, Bhiksha
Smaragdis, Paris
author_sort Shashanka, Madhusudana
collection PubMed
description This paper presents a family of probabilistic latent variable models that can be used for analysis of nonnegative data. We show that there are strong ties between nonnegative matrix factorization and this family, and provide some straightforward extensions which can help in dealing with shift invariances, higher-order decompositions and sparsity constraints. We argue through these extensions that the use of this approach allows for rapid development of complex statistical models for analyzing nonnegative data.
format Text
id pubmed-2391276
institution National Center for Biotechnology Information
language English
publishDate 2008
publisher Hindawi Publishing Corporation
record_format MEDLINE/PubMed
spelling pubmed-23912762008-05-28 Probabilistic Latent Variable Models as Nonnegative Factorizations Shashanka, Madhusudana Raj, Bhiksha Smaragdis, Paris Comput Intell Neurosci Research Article This paper presents a family of probabilistic latent variable models that can be used for analysis of nonnegative data. We show that there are strong ties between nonnegative matrix factorization and this family, and provide some straightforward extensions which can help in dealing with shift invariances, higher-order decompositions and sparsity constraints. We argue through these extensions that the use of this approach allows for rapid development of complex statistical models for analyzing nonnegative data. Hindawi Publishing Corporation 2008 2008-05-11 /pmc/articles/PMC2391276/ /pubmed/18509481 http://dx.doi.org/10.1155/2008/947438 Text en Copyright © 2008 Madhusudana Shashanka et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Shashanka, Madhusudana
Raj, Bhiksha
Smaragdis, Paris
Probabilistic Latent Variable Models as Nonnegative Factorizations
title Probabilistic Latent Variable Models as Nonnegative Factorizations
title_full Probabilistic Latent Variable Models as Nonnegative Factorizations
title_fullStr Probabilistic Latent Variable Models as Nonnegative Factorizations
title_full_unstemmed Probabilistic Latent Variable Models as Nonnegative Factorizations
title_short Probabilistic Latent Variable Models as Nonnegative Factorizations
title_sort probabilistic latent variable models as nonnegative factorizations
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2391276/
https://www.ncbi.nlm.nih.gov/pubmed/18509481
http://dx.doi.org/10.1155/2008/947438
work_keys_str_mv AT shashankamadhusudana probabilisticlatentvariablemodelsasnonnegativefactorizations
AT rajbhiksha probabilisticlatentvariablemodelsasnonnegativefactorizations
AT smaragdisparis probabilisticlatentvariablemodelsasnonnegativefactorizations

Ejemplares similares