Co-clustering under nonnegative matrix tri-factorization

Authors:
Lazhar Labiod;Mohamed Nadif
Affiliations:
LIPADE, Université Paris Descartes, Paris, France;LIPADE, Université Paris Descartes, Paris, France
Venue:
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Year:
2011

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Abstract

The nonnegative matrix tri-factorization (NMTF) approach has recently been shown to be useful and effective to tackle the co-clustering. In this work, we embed this problem in the NMF framework and we derive from the double k-means objective function a new formulation of the criterion. To optimize it, we develop two algorithms based on two multiplicative update rules. In addition we show that the double k-means is equivalent to algebraic problem of NMF under some suitable constraints. Numerical experiments on simulated and real datasets demonstrate the interest of our approach.