Presentation 2005/10/11
Analysis of Subspace Bayes Approach in Linear Neural Networks : Relation between Baysian Approach and Shrinkage Estimation
Shinichi NAKAJIMA, Sumio WATANABE,
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Abstract(in English) It is well known that the generalization performance of unidentifiable models differs from that of the regular models. According to recent works, it is known that the Bayes estimation has the advantage over the maximum likelihood estimation. However, accurate approximation of the posterior distribution requires huge computational costs. In this paper, we consider an alternative approximation method, which we call a subspace Bayes approach, and discuss the relation to the shrinkage estimation and the variational Bayes approach. We show that, in three-layer linear neural networks, the subspace Bayes approach is asymptotically equivalent to a positive-part James-Stein type shrinkage estimation, that it provides as good generalization performance as the Bayes estimation in typical cases, and that it is strongly related to the variational Bayes approach.
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Keyword(in English) subspace Bayes / empirical Bayes / variational Bayes / shrinkage / neural networks / singular model
Paper # NC2005-49
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Committee NC
Conference Date 2005/10/11(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Analysis of Subspace Bayes Approach in Linear Neural Networks : Relation between Baysian Approach and Shrinkage Estimation
Sub Title (in English)
Keyword(1) subspace Bayes
Keyword(2) empirical Bayes
Keyword(3) variational Bayes
Keyword(4) shrinkage
Keyword(5) neural networks
Keyword(6) singular model
1st Author's Name Shinichi NAKAJIMA
1st Author's Affiliation Tokyo Institute of Technology:Nikon Corporation()
2nd Author's Name Sumio WATANABE
2nd Author's Affiliation Tokyo Institute of Technology
Date 2005/10/11
Paper # NC2005-49
Volume (vol) vol.105
Number (no) 342
Page pp.pp.-
#Pages 6
Date of Issue