A Feature Space View of Spectral Clustering
نویسنده
چکیده
The transductive SVM is a semi-supervised learning algorithm that searches for a large margin hyperplane in feature space. By withholding the training labels and adding a constraint that favors balanced clusters, it can be turned into a clustering algorithm. The Normalized Cuts clustering algorithm of Shi and Malik, although originally presented as spectral relaxation of a graph cut problem, can be interpreted as a relaxation on clustering with transductive SVMs. Using this interpretation of Normalized Cuts, we identify some of its weaknesses, and propose a correction on it.
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