Semi-supervised clustering via constrained symmetric non-negative matrix factorization
BI'12 Proceedings of the 2012 international conference on Brain Informatics
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Semi-supervised nonnegative matrix factorization (NMF)receives more and more attention in text mining field. The semi-supervised NMF methods can be divided into two types, one is based on the explicit category labels, the other is based on the pair wise constraints including must-link and cannot-link. As it is hard to obtain the category labels in some tasks, the latter one is more widely used in real applications. To date, all the constrained NMF methods treat the must-link and cannot-link constraints in a same way. However, these two kinds of constraints play different roles in NMF clustering. Thus a novel constrained NMF method is proposed in this paper. In the new method, must-link constraints are used to control the distance of the data in the compressed form, and cannot-ink constraints are used to control the encoding factor. Experimental results on real-world text data sets have shown the good performance of the proposed method.