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...

• Type: Conference
• Year: 2004
• Where: ICML
• Authors: Ting Su, Jennifer G. Dy
• Keywords: Automated Hierarchical Mixture, Gaussian Cluster Component, ICML 2004, Machine Learning, Principal Component Dimensions