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Probabilistic Latent Variable Models as Nonnegative Factorizations
Joint Authors
Smaragdis, Paris
Shashanka, Madhusudana
Raj, Bhiksha
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2008, Issue 2008 (31 Dec. 2008), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2008-05-15
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Shashanka, Madhusudana& Raj, Bhiksha& Smaragdis, Paris. 2008. Probabilistic Latent Variable Models as Nonnegative Factorizations. Computational Intelligence and Neuroscience،Vol. 2008, no. 2008, pp.1-8.
https://search.emarefa.net/detail/BIM-510478
Modern Language Association (MLA)
Shashanka, Madhusudana…[et al.]. Probabilistic Latent Variable Models as Nonnegative Factorizations. Computational Intelligence and Neuroscience No. 2008 (2008), pp.1-8.
https://search.emarefa.net/detail/BIM-510478
American Medical Association (AMA)
Shashanka, Madhusudana& Raj, Bhiksha& Smaragdis, Paris. Probabilistic Latent Variable Models as Nonnegative Factorizations. Computational Intelligence and Neuroscience. 2008. Vol. 2008, no. 2008, pp.1-8.
https://search.emarefa.net/detail/BIM-510478
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-510478