Semidefinite spectral clustering

doi:10.1016/j.patcog.2006.05.021

2006-11-01

PRE

Jaehwan Kim

Seungjin Choi *

DOI：10.1016/j.patcog.2006.05.021

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摘要

En 中文

Multi-way partitioning of an undirected weighted graph where pairwise similarities are assigned as edge weights, provides an important tool for data clustering, but is an NP-hard problem. Spectral relaxation is a popular way of relaxation, leading to spectral clustering where the clustering is peformed by the eigen-decomposition of the (normalized) graph Laplacian. On the other hand, semidefinite relaxation, is an alternative way of relaxing a combinatorial optimization, leading to a convex optimization. In this paper we employ a semidefinite programming (SDP) approach to the graph equipartitioning for clustering, where sufficient conditions for strong duality hold. The method is referred to as semidefinite spectral clustering, where the clustering is based on the eigen-decomposition of the optimal feasible matrix computed by SDR Numerical experiments with several data sets, demonstrate the useful behavior of our semidefinite spectral clustering, compared to existing spectral clustering methods. (c) 2006 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

Keyword:

clustering

convex optimization

multi-way graph equipartitioning

semidefinite programming

spectral clustering