Abstract
Several variants of Nonnegative Matrix Factorization (NMF) have been proposed for supervised classification of various objects. Graph regularized NMF (GNMF) incorporates the information on the data geometric structure to the training process, which considerably improves the classification results. However, the multiplicative algorithms used for updating the underlying factors may result in a slow convergence of the training process. To tackle this problem, we propose to use the Spectral Projected Gradient (SPG) method that is based on quasi-Newton methods. The results are presented for image classification problems.
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Zdunek, R., Phan, AH., Cichocki, A. (2013). GNMF with Newton-Based Methods. In: Mladenov, V., Koprinkova-Hristova, P., Palm, G., Villa, A.E.P., Appollini, B., Kasabov, N. (eds) Artificial Neural Networks and Machine Learning – ICANN 2013. ICANN 2013. Lecture Notes in Computer Science, vol 8131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40728-4_12
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DOI: https://doi.org/10.1007/978-3-642-40728-4_12
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