Peter Meer , Rutgers University, Piscataway
Oncel Tuzel , Mitsubishi Electric Research Laboratories, Cambridge
Sushil Mittal , Scibler Technologies, Bangalore
Saket Anand , Rutgers University, Piscataway
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.
Vectors, Clustering algorithms, Null space, Symmetric matrices, Clustering methods, Computer vision, Algorithms, Clustering, Similarity measures, Computer vision, Applications, Pattern Recognition, Computing Methodologies
Peter Meer, Oncel Tuzel, Sushil Mittal, Saket Anand, "Semi-Supervised Kernel Mean Shift Clustering", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. , no. , pp. 0, 5555, doi:10.1109/TPAMI.2013.190