Nonnegative Matrix Factorization with Gaussian Process Priors
Joint Authors
Laurberg, Hans
Schmidt, Mikkel N.
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2008, Issue 2008 (31 Dec. 2008), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2008-04-21
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
We present a general method for including prior knowledge in a nonnegative matrix factorization (NMF), based on Gaussian process priors.
We assume that the nonnegative factors in the NMF are linked by a strictly increasing function to an underlying Gaussian process specified by its covariance function.
This allows us to find NMF decompositions that agree with our prior knowledge of the distribution of the factors, such as sparseness, smoothness, and symmetries.
The method is demonstrated with an example from chemical shift brain imaging.
American Psychological Association (APA)
Schmidt, Mikkel N.& Laurberg, Hans. 2008. Nonnegative Matrix Factorization with Gaussian Process Priors. Computational Intelligence and Neuroscience،Vol. 2008, no. 2008, pp.1-10.
https://search.emarefa.net/detail/BIM-465919
Modern Language Association (MLA)
Schmidt, Mikkel N.& Laurberg, Hans. Nonnegative Matrix Factorization with Gaussian Process Priors. Computational Intelligence and Neuroscience No. 2008 (2008), pp.1-10.
https://search.emarefa.net/detail/BIM-465919
American Medical Association (AMA)
Schmidt, Mikkel N.& Laurberg, Hans. Nonnegative Matrix Factorization with Gaussian Process Priors. Computational Intelligence and Neuroscience. 2008. Vol. 2008, no. 2008, pp.1-10.
https://search.emarefa.net/detail/BIM-465919
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-465919