A Local Learning Approach for Clustering

Wu, M; Schölkopf, B; Schölkopf,; B.,; Platt, J.; Hofmann, T.

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A Local Learning Approach for Clustering

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Wu, M
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Schölkopf, B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Wu, M., & Schölkopf, B. (2007). A Local Learning Approach for Clustering. Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference, 1529-1536.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-CBCD-0

Abstract

We present a local learning approach for clustering. The basic idea is that a good clustering result should have the property that the cluster label of each data point can be well predicted based on its neighboring data and their cluster labels, using current supervised learning methods. An optimization problem is formulated such that its solution has the above property. Relaxation and eigen-decomposition are applied to solve this optimization problem. We also briefly investigate the parameter selection issue and provide a simple parameter selection method for the proposed algorithm. Experimental results are provided to validate the effectiveness of the proposed approach.