Collective matrix factorization for co-clustering

Authors:
Mrinmaya Sachan;Shashank Srivastava
Affiliations:
Carnegie Mellon University, Pittsburgh, PA, USA;Carnegie Mellon University, Pittsburgh, PA, USA
Venue:
Proceedings of the 22nd international conference on World Wide Web companion
Year:
2013

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Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining

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Abstract

We outline some matrix factorization approaches for co- clustering polyadic data (like publication data) using non-negative factorization (NMF). NMF approximates the data as a product of non-negative low-rank matrices, and can induce desirable clustering properties in the matrix factors through a flexible range of constraints. We show that simultaneous factorization of one or more matrices provides potent approaches for co-clustering.