The Translation-invariant Wishart-Dirichlet Process for Clustering Distance Data

We present a probabilistic model for clustering of objects represented via pairwise dissimilarities. We propose that even if an underlying vectorial representation exists, it is better to work directly with the dissimilarity matrix hence avoiding unnecessary bias and variance caused by embeddings. By using a Dirichlet process prior we are not obliged to fix the number of clusters in advance. Furthermore, our clustering model is permutation-, scale- and translation-invariant, and it is called the Translation-invariant Wishart Dirichlet (TIWD) process. A highly efficient MCMC sampling algorithm is presented. Experiments show that the TIWD process exhibits several advantages over competing approaches.

• Type: Conference
• Year: 2010
• Where: ICML
• Authors: Julia E. Vogt, Sandhya Prabhakaran, Thomas J. Fuchs, Volker Roth
• Keywords: Dirichlet Process, ICML 2010, Machine Learning, Pairwise Dissimilarities, Translation-invariant Wishart Dirichlet