한국정보과학회:학술대회논문집 (Proceedings of the Korean Information Science Society Conference)
- 한국정보과학회 2005년도 한국컴퓨터종합학술대회 논문집 Vol.32 No.1 (A)
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- Pages.892-894
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- 2005
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- 1598-5164(pISSN)
준정부호 스펙트럼의 군집화
Semidefinite Spectral Clustering
- Kim, Jae-Hwan (Department of Computer Science, POSTECH) ;
- Choi, Seung-Jin (Department of Computer Science, POSTECH)
- 발행 : 2005.07.01
초록
Graph partitioning provides an important tool for data clustering, but is an NP-hard combinatorial optimization problem. Spectral clustering where the clustering is performed by the eigen-decomposition of an affinity matrix [1,2]. This is a popular way of solving the graph partitioning problem. On the other hand, semidefinite relaxation, is an alternative way of relaxing combinatorial optimization. issuing to a convex optimization[4]. In this paper we present a semidefinite programming (SDP) approach to graph equi-partitioning for clustering and then we use eigen-decomposition to obtain an optimal partition set. Therefore, the method is referred to as semidefinite spectral clustering (SSC). Numerical experiments with several artificial and real data sets, demonstrate the useful behavior of our SSC. compared to existing spectral clustering methods.
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