Semi-supervised kernel mean shift clustering

Saket Anand, Sushil Mittal, Oncel Tuzel, Peter Meer

Research output: Contribution to journalArticlepeer-review

105 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number6616555
Pages (from-to)1201-1215
Number of pages15
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume36
Issue number6
DOIs
StatePublished - Jun 2014

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

Keywords

  • Semi-supervised kernel clustering
  • log det Bregman divergence
  • mean shift clustering

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