Convergence of the Wake-Sleep algorithm

被引：0

作者：

Ikeda, S ^{[1
]}

Amari, S ^{[1
]}

Nakahara, H ^{[1
]}

机构：

[1] PRESTO, JST, Wako, Saitama 3510198, Japan

来源：

ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 11 | 1999年 / 11卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The W-S (Wake-Sleep) algorithm is a simple learning rule for the models with hidden variables. It is shown that this algorithm can be applied to a factor analysis model which is a linear version of the Helmholtz machine. But even for a factor analysis model, the general convergence is not proved theoretically. In this article, we describe the geometrical understanding of the W-S algorithm in contrast with the EM (Expectation-Maximization) algorithm and the em algorithm. As the result, we prove the convergence of the W-S algorithm for the factor analysis model. We also show the condition for the convergence in general models.

引用

页码：239 / 245

页数：7

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