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.
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Labiod, L., Nadif, M. (2011). Co-clustering under Nonnegative Matrix Tri-Factorization. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7063. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24958-7_82
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DOI: https://doi.org/10.1007/978-3-642-24958-7_82
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24957-0
Online ISBN: 978-3-642-24958-7
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