Timezone: »

Probabilistic Low-Rank Subspace Clustering
S. Derin Babacan · Shinichi Nakajima · Minh Do

Thu Dec 06 02:00 PM -- 12:00 AM (PST) @ Harrah’s Special Events Center 2nd Floor

In this paper, we consider the problem of clustering data points into low-dimensional subspaces in the presence of outliers. We pose the problem using a density estimation formulation with an associated generative model. Based on this probability model, we first develop an iterative expectation-maximization (EM) algorithm and then derive its global solution. In addition, we develop two Bayesian methods based on variational Bayesian (VB) approximation, which are capable of automatic dimensionality selection. While the first method is based on an alternating optimization scheme for all unknowns, the second method makes use of recent results in VB matrix factorization leading to fast and effective estimation. Both methods are extended to handle sparse outliers for robustness and can handle missing values. Experimental results suggest that proposed methods are very effective in clustering and identifying outliers.

Author Information

S. Derin Babacan (Google Research)
Shinichi Nakajima (TU Berlin)
Minh Do (University of Illinois)

More from the Same Authors