Semi-Supervised Kernel Mean Shift Clustering.

Saket Anand(1), Sushil Mittal(2), Oncel Tuzel(3) and Peter Meer(1)
(1) Department of Electrical and Computer Engineering
Rutgers University, Piscataway, NJ 08854, USA
(2) Scibler Technologies, Bangalore, India
(3) Mitsubishi Electric Research Laboratories
Cambridge, MA 02139

Mean shift clustering is a powerful nonparametric technique that does not require prior knowledge of the number of clusters and does not constrain the shape of the clusters. However, being completely unsupervised, its performance suffers when the original distance metric fails to capture the underlying cluster structure. Despite recent advances in semi-supervised clustering methods, there has been little effort towards incorporating supervision into mean shift. We propose a semi-supervised framework for kernel mean shift clustering (SKMS) that uses only pairwise constraints to guide the clustering procedure. The points are first mapped to a high-dimensional kernel space where the constraints are imposed by a linear transformation of the mapped points. This is achieved by modifying the initial kernel matrix by minimizing a log det divergence-based objective function.We show the advantages of SKMS by evaluating its performance on various synthetic and real datasets while comparing with state-of-the-art semi-supervised clustering algorithms.

IEEE Trans. Pattern Anal. Machine Intell., 36, 1201-1215, 2014.
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