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Nonparametric Orthogonal NMF and its Application in Cancer Clustering

  • Andri Mirzal
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 285)

Abstract

Orthogonal nonnegative matrix factorization (NMF) is an NMF objective function that enforces orthogonality constraint on its factor. There are two challenges in optimizing this objective function: the first is how to design an algorithm that has convergence guarantee, and the second is how to automatically choose the regularization parameter. In our previous work, we have been able to develop a convergent algorithm for this objective function. However, the second challenge remains unsolved. In this paper, we provide an attempt to answer the second challenge. The proposed method is based on the L-curve approach and has a simple form which is preferable since it introduces only a small additional computational cost. This method transforms the algorithm into nonparametric, and is also extendable to other NMF objective functions as long as the functions are differentiable with respect to the corresponding regularization parameters. Numerical results are then provided to evaluate the feasibility of the method in choosing the appropriate regularization parameter values by utilizing it in cancer clustering tasks.

Keywords

Cancer clustering Gene expression Nonnegative matrix factorization Nonparametric learning Orthogonality constraint 

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Notes

Acknowledgments

The author would like to thank the reviewers for useful comments. This research was supported by Ministry of Higher Education of Malaysia and Universiti Teknologi Malaysia under Exploratory Research Grant Scheme R.J130000.7828.4L095.

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Copyright information

© Springer Science+Business Media Singapore 2014

Authors and Affiliations

  1. 1.Faculty of Computing, N28-439-03Universiti Teknologi Malaysia UTMJohor BahruMalaysia

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