期刊
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
卷 32, 期 2, 页码 335-347出版社
IEEE COMPUTER SOC
DOI: 10.1109/TPAMI.2008.292
关键词
Spectral clustering; kernel principal component analysis; out-of-sample extensions; model selection
资金
- Research Council K. U. Leuven [GOA-Mefisto 666, GOA-Ambiorics]
- Flemish Government FWO [G.0240.99, G.0211.05, G.0407.02, G.0197.02, G.0080.01, G.0141.03, G.0491.03, G.0120.03, G.0452.04, G .0499.04, G.0226.06, G.0302.07]
- ICCoS
- ANMMM
- AWI
- IWT
- GBOU (McKnow) Soft4s
- Belgian Federal Government (Belgian Federal Science Policy Office [IUAP V-22, PODO-II (CP/01/40]
- EU
- Contracts Research/Agreements (ISMC/IPCOS, Data4s, TML, Elia, LMS, IPCOS, Mastercard).
A new formulation for multiway spectral clustering is proposed. This method corresponds to a weighted kernel principal component analysis (PCA) approach based on primal-dual least-squares support vector machine (LS-SVM) formulations. The formulation allows the extension to out-of-sample points. In this way, the proposed clustering model can be trained, validated, and tested. The clustering information is contained on the eigendecomposition of a modified similarity matrix derived from the data. This eigenvalue problem corresponds to the dual solution of a primal optimization problem formulated in a high-dimensional feature space. A model selection criterion called the Balanced Line Fit (BLF) is also proposed. This criterion is based on the out-of-sample extension and exploits the structure of the eigenvectors and the corresponding projections when the clusters are well formed. The BLF criterion can be used to obtain clustering parameters in a learning framework. Experimental results with difficult toy problems and image segmentation show improved performance in terms of generalization to new samples and computation times.
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