A variational Bayesian method for rectified factor analysis (2005)
| Venue: | In Proc. 2005 IEEE Int. Joint Conf. on Neural Networks (IJCNN 2005 |
| Citations: | 5 - 4 self |
BibTeX
@INPROCEEDINGS{Harva05avariational,
author = {Markus Harva},
title = {A variational Bayesian method for rectified factor analysis},
booktitle = {In Proc. 2005 IEEE Int. Joint Conf. on Neural Networks (IJCNN 2005},
year = {2005},
pages = {185--190}
}
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Abstract
Abstract — Linear factor models with nonnegativity constraints have received a great deal of interest in a number of problem domains. In existing approaches, positivity has often been associated with sparsity. In this paper we argue that sparsity of the factors is not always a desirable option, but certainly a technical limitation of the currently existing solutions. We then reformulate the problem in order to relax the sparsity constraint while retaining positivity. A variational inference procedure is derived and this is contrasted to existing related approaches. Both i.i.d. and first-order AR variants of the proposed model are provided and these are experimentally demonstrated in a real-world astrophysical application. I.
