Pairwise constraints specify whether or not two samples should be in one cluster. Although it has been successful to incorporate them into traditional clustering methods, such as K-means, little progress has been made in combining them with spectral clustering. The major challenge in designing an effective constrained spectral clustering is a sensible combination of the scarce pairwise constraints with the original af\ufb01nity matrix. We propose to combine the two sources of af\ufb01nity by propagating the pairwise constraints information over the original af\ufb01nity matrix. Our method has a Gaussian process interpretation and results in a closed-form expression for the new af\ufb01nity matrix. Experiments show it outperforms state-of-the-art constrained clustering methods in getting good clusterings with fewer constraints, and yields good image segmentation with userspeci\ufb01ed pairwise constraints.<\/p>\n","protected":false},"excerpt":{"rendered":"
Pairwise constraints specify whether or not two samples should be in one cluster. Although it has been successful to incorporate them into traditional clustering methods, such as K-means, little progress has been made in combining them with spectral clustering. 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