Local factor analysis with automatic model selection: A comparative study and digits recognition application
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AbstractA 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.
All Author(s) ListShi L, Xu L
Name of Conference16th International Conference on Artificial Neural Networks (ICANN 2006)
Start Date of Conference10/09/2006
End Date of Conference14/09/2006
Place of ConferenceAthens
Journal nameLecture Notes in Artificial Intelligence
Detailed descriptionorganized by National Technical University of Athens (NTUA),
Volume Number4132
Pages260 - 269
LanguagesEnglish-United Kingdom
Web of Science Subject CategoriesComputer Science; Computer Science, Artificial Intelligence; Computer Science, Theory & Methods

Last updated on 2021-15-09 at 00:44