Constraints as Features

In this paper, we introduce a new approach to con-strained clustering which treats the constraints as features. Our method augments the original feature space with ad-ditional dimensions, each of which derived from a given Cannot-link constraints. The specified Cannot-link pair gets extreme coordinates values, and the rest of the points get coordinate values that express their spatial influence from the specified constrained pair. After augmenting all the new features, a standard unconstrained clustering al-gorithm can be performed, like k-means or spectral clus-tering. We demonstrate the efficacy of our method for ac-tive semi-supervised learning applied to image segmenta-tion and compare it to alternative methods. We also eval-uate the performance of our method on the four most com-monly evaluated datasets from the UCI machine learning repository. 1