Constrained clustering via spectral regularization

We propose a novel framework for constrained spectral clustering with pairwise constraints which specify whether two objects belong to the same cluster or not. Unlike previous methods that modify the similarity matrix with pairwise constraints, we adapt the spectral embedding towards an ideal embedd...

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Bibliographic Details
Published in2009 IEEE Conference on Computer Vision and Pattern Recognition pp. 421 - 428
Main Authors Zhenguo Li, Jianzhuang Liu, Xiaoou Tang
Format Conference Proceeding
LanguageEnglish
Japanese
Published IEEE 01.06.2009
Subjects
Clustering algorithms
Computer vision
Current measurement
Data analysis
Data mining
Feature extraction
Glass
Kernel
Large-scale systems
Pattern recognition
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Summary:We propose a novel framework for constrained spectral clustering with pairwise constraints which specify whether two objects belong to the same cluster or not. Unlike previous methods that modify the similarity matrix with pairwise constraints, we adapt the spectral embedding towards an ideal embedding as consistent with the pairwise constraints as possible. Our formulation leads to a small semidefinite program whose complexity is independent of the number of objects in the data set and the number of pairwise constraints, making it scalable to large-scale problems. The proposed approach is applicable directly to multi-class problems, handles both must-link and cannot-link constraints, and can effectively propagate pairwise constraints. Extensive experiments on real image data and UCI data have demonstrated the efficacy of our algorithm.
ISBN:1424439922
9781424439928
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2009.5206852