Local Factor Analysis with Automatic Model Selection: A Comparative Study and Digits Recognition Application
Identifieur interne : 001067 ( Main/Merge ); précédent : 001066; suivant : 001068Local Factor Analysis with Automatic Model Selection: A Comparative Study and Digits Recognition Application
Auteurs : Lei Shi [Hong Kong] ; Lei Xu [Hong Kong]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2006.
Abstract
Abstract: A further investigation is made on an adaptive local factor analysis algorithm from Bayesian Ying-Yang (BYY) harmony learning, which makes parameter learning with automatic determination of both the component number and the factor number in each component. A comparative study has been conducted on simulated data sets and several real problem data sets. The algorithm has been compared with not only a recent approach called Incremental Mixture of Factor Analysers (IMoFA) but also the conventional two-stage implementation of maximum likelihood (ML) plus model selection, namely, using the EM algorithm for parameter learning on a series candidate models, and selecting one best candidate by AIC, CAIC, and BIC. Experiments have shown that IMoFA and ML-BIC outperform ML-AIC or ML-CAIC while the BYY harmony learning considerably outperforms IMoFA and ML-BIC. Furthermore, this BYY learning algorithm has been applied to the popular MNIST database for digits recognition with a promising performance.
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DOI: 10.1007/11840930_27
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