Spectral clustering: A semi-supervised approach |
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Authors: | Weifu ChenAuthor Vitae Guocan FengAuthor Vitae |
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Affiliation: | a School of Mathematics and Computational Science, Sun Yat-sen University, 510275 Guangzhou, China b Guangdong Province Key Laboratory of Computational Science, Guangzhou, China |
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Abstract: | Recently, graph-based spectral clustering algorithms have been developing rapidly, which are proposed as discrete combinatorial optimization problems and approximately solved by relaxing them into tractable eigenvalue decomposition problems. In this paper, we first review the current existing spectral clustering algorithms in a unified-framework way and give a straightforward explanation about spectral clustering. We also present a novel model for generalizing the unsupervised spectral clustering to semi-supervised spectral clustering. Under this model, prior information given by some instance-level constraints can be generalized to space-level constraints. We find that (undirected) graph built on the enlarged prior information is more meaningful, hence the boundaries of the clusters are more correct. Experimental results based on toy data, real-world data and image segmentation demonstrate the advantages of the proposed model. |
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Keywords: | Spectral clustering Semi-supervised clustering Pairwise constraint Distortion measure |
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