Mixture of Probabilistic Factor Analysis Model and Its Applications

Authors:
Masahiro Tanaka
Affiliations:
-
Venue:
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Year:
2001

Citing 1
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Abstract

In this paper, the regression analysis is treated when the output estimate may take more than one value. This is an extension of the usual regression analysis and such cases may happen when the output is affected by some unknown input. The stochastic model used in this paper is the mixture of probabilistic factor analysis model whose identification scheme has been already developed by Tipping and Bishop. We will show the usefulness of our method by a numerical example.