Constrained clustering by spectral kernel learning

Zhenguo Li, Jianzhuang Liu
2009 2009 IEEE 12th International Conference on Computer Vision  
Clustering performance can often be greatly improved by leveraging side information. In this paper, we consider constrained clustering with pairwise constraints, which specify some pairs of objects from the same cluster or not. The main idea is to design a kernel to respect both the proximity structure of the data and the given pairwise constraints. We propose a spectral kernel learning framework and formulate it as a convex quadratic program, which can be optimally solved efficiently. Our
more » ... work enjoys several desirable features: 1) it is applicable to multi-class problems; 2) it can handle both must-link and cannot-link constraints; 3) it can propagate pairwise constraints effectively; 4) it is scalable to large-scale problems; and 5) it can handle weighted pairwise constraints. Extensive experiments have demonstrated the superiority of the proposed approach.
doi:10.1109/iccv.2009.5459157 dblp:conf/iccv/LiL09 fatcat:qbqhssycdjflfcnrnj6mzf55qu