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Co-clustering can be viewed as a two-way (bilinear) factorization of a large data matrix into dense/uniform and possibly overlapping sub-matrix factors (co-clusters). This combinatorially complex problem emerges in several applications, including behavior inference tasks encountered with social networks. Existing co-clustering schemes do not exploit the fact that overlapping factors are often sparse,...
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