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2009 IEEE Conference on Computer Vision and Pattern Recognition
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 thedoi:10.1109/cvprw.2009.5206852 fatcat:ho6v5kxgzjh6lgrwmzy5fe4jam