Subspace clustering with the multivariate-t distribution

Clustering procedures suitable for the analysis of very high-dimensional data are needed for many modern data sets. One approach, called high-dimensional data clustering (HDDC), uses a family of Gaussian mixture models for clustering. HDDC is based on the idea that high-dimensional data usually exists in lower-dimensional subspaces; as such, an intrinsic dimension for each sub-population of the observed data can be estimated and cluster …