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Semi-Supervised Kernel Mean Shift Clustering
June 2014 (vol. 36 no. 6)
pp. 1-1
Oncel Tuzel, , Mitsubishi Electric Research Laboratories, Cambridge, MA, USA
Sushil Mittal, , Scibler Corporation, Santa Clara, CA, USA
Saket Anand, Electrical and Computer Engineering Department, Rutgers University
Peter Meer, Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, USA
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.
Index Terms:
Vectors,Clustering algorithms,Null space,Symmetric matrices,Clustering methods,Computer vision,Algorithms,Clustering,Similarity measures,Computer vision,Applications,Pattern Recognition,Computing Methodologies
Citation:
Oncel Tuzel, Sushil Mittal, Saket Anand, Peter Meer, "Semi-Supervised Kernel Mean Shift Clustering," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 36, no. 6, pp. 1-1, June 2014, doi:10.1109/TPAMI.2013.190
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