Probabilistic Latent Variable Models as Nonnegative Factorizations | Zendy

Madhusudana Shashanka | Zendy; Bhiksha Raj | Zendy; Paris Smaragdis | Zendy

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Probabilistic Latent Variable Models as Nonnegative Factorizations

Author(s) -

Madhusudana Shashanka,

Bhiksha Raj,

Paris Smaragdis

Publication year - 2008

Publication title -

computational intelligence and neuroscience

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.605

H-Index - 52

eISSN - 1687-5273

pISSN - 1687-5265

DOI - 10.1155/2008/947438

Subject(s) - non negative matrix factorization , latent variable , probabilistic logic , variable (mathematics) , computer science , latent variable model , statistical model , factorization , order (exchange) , matrix decomposition , artificial intelligence , mathematics , algorithm , mathematical analysis , eigenvalues and eigenvectors , physics , finance , quantum mechanics , economics

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.

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