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ICML
2004
IEEE

Automated hierarchical mixtures of probabilistic principal component analyzers

15 years 8 days ago
Automated hierarchical mixtures of probabilistic principal component analyzers
Many clustering algorithms fail when dealing with high dimensional data. Principal component analysis (PCA) is a popular dimensionality reduction algorithm. However, it assumes a single multivariate Gaussian model, which provides a global linear projection of the data. Mixture of probabilistic principal component analyzers (PPCA) provides a better model to the clustering paradigm. It provides a local linear PCA projection for each multivariate Gaussian cluster component. We extend this model to build hierarchical mixtures of PPCA. Hierarchical clustering provides a flexible representation showing relationships among clusters in various perceptual levels. We introduce an automated hierarchical mixture of PPCA algorithm, which utilizes the integrated classification likelihood as a criterion for splitting and stopping the addition of hierarchical levels. An automated approach requires automated methods for initialization, determining the number of principal component dimensions, and dete...
Ting Su, Jennifer G. Dy
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2004
Where ICML
Authors Ting Su, Jennifer G. Dy
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