Name
Title | ||
|---|---|---|
A comparative study on selection of cluster number and local subspace dimension in the mixture PCA models |
Abstract | ||
|---|---|---|
How to determine the number of clusters and the dimensions of local principal subspaces is an important and challenging problem in various applications. Based on a probabilistic model of local PCA, this problem can be solved by one of existing statistical model selection criteria in a two-phase procedure. However, such a two-phase procedure is too time-consuming especially when there is no prior knowledge. The BYY harmony learning has provided a promising mechanism to make automatic model selection in parallel with parameter learning. This paper investigates the BYY harmony learning with automatic model selection on a mixture PCA model in comparison with three typical model selection criteria: AIC, CAIC, and MDL. This comparative study is made by experiments for different model selection tasks on simulated data sets under different conditions. Experiments have shown that automatic model selection by the BYY harmony learning are not only as good as or even better than conventional methods in terms of performances, but also considerably supervisory in terms of much less computational cost. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11759966_180 | ISNN (1) |
Keywords | Field | DocType |
two-phase procedure,statistical model selection criterion,typical model selection criterion,automatic model selection,mixture pca model,byy harmony,local subspace dimension,probabilistic model,comparative study,different model selection task,cluster number,parameter learning,byy harmony learning,statistical model,model selection | Pattern recognition,Subspace topology,Computer science,Minimum description length,Determining the number of clusters in a data set,Model selection,Statistical model,Artificial intelligence,Artificial neural network,Mixture theory,Principal component analysis,Machine learning | Conference |
Volume | ISSN | ISBN |
3971 | 0302-9743 | 3-540-34439-X |
Citations | PageRank | References |
1 | 0.35 | 6 |
Authors | ||
2 | ||