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 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.
Shashanka
Madhusudana
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Mars Incorporated, 800 High Street, Hackettstown, New Jersy 07840, USA.
Raj
Bhiksha
B
Smaragdis
Paris
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