Abstract
Nonnegative Matrix Factorization (NMF) is broadly used as a mathematical tool for processing tasks of tabulated data. In this paper, an extension of NMF based on a generalized product rule, defined with a nonlinear one-parameter function and its inverse, is proposed. From a viewpoint of subspace methods, the extended NMF constructs flexible subspaces which plays an important role in classification tasks. Experimental results on benchmark datasets show that the proposed extension improves classification accuracies.
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Fujimoto, Y., Murata, N. (2012). Nonnegative Matrix Factorization via Generalized Product Rule and Its Application for Classification. In: Theis, F., Cichocki, A., Yeredor, A., Zibulevsky, M. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2012. Lecture Notes in Computer Science, vol 7191. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28551-6_33
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DOI: https://doi.org/10.1007/978-3-642-28551-6_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28550-9
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