Name
Title | ||
|---|---|---|
Local factor analysis with automatic model selection: a comparative study and digits recognition application |
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. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11840930_27 | ICANN (2) |
Keywords | Field | DocType |
real problem data set,comparative study,adaptive local factor analysis,digits recognition application,best candidate,automatic model selection,factor number,parameter learning,harmony learning,component number,em algorithm,byy harmony,series candidate model,maximum likelihood,factor analysis,model selection | MNIST database,Pattern recognition,Expectation–maximization algorithm,Computer science,Minimum description length,Model selection,Artificial intelligence,Estimation theory,Adaptive algorithm,Artificial neural network,Mixture model,Machine learning | Conference |
Volume | ISSN | ISBN |
4132 | 0302-9743 | 3-540-38871-0 |
Citations | PageRank | References |
3 | 0.39 | 10 |
Authors | ||
2 | ||