Graph regularized NMF (GNMF) incorporates the information on the data geometric structure to the training process, which considerably improves the ...
Abstract. Several variants of Nonnegative Matrix Factorization (NMF) have been proposed for supervised classification of various objects. Graph.
This work proposes to use the Spectral Projected Gradient (SPG) method that is based on quasi-Newton methods for image classification problems and presents ...
Sep 18, 2016 · Graph regularized NMF (GNMF) incorporates the information on the data geometric structure to the training process, which considerably improves ...
Abstract. Several variants of Nonnegative Matrix Factorization (NMF) have been proposed for supervised classification of various objects. Graph.
Graph regularized NMF (GNMF) incorporates the information on the data geometric structure to the training process, which considerably improves the ...
Graph regularized nonnegative matrix factorization (GNMF) decomposes a nonnegative data matrix to the product of two lower-rank nonnegative factor matrices, ...
Hybrid methods switch between alternation and Newton formulations as iterations progress, so that the periods of rapid convergence exhibited by alter- nation�...
Missing: GNMF | Show results with:GNMF
Jul 25, 2017 · This paper presents a second-order method that modifies the update of Newton's method by replacing the negative eigenvalues of the Hessian by their absolute ...
Missing: GNMF | Show results with:GNMF
"GNMF with Newton-Based Methods." In Artificial Neural Networks and Machine Learning – ICANN 2013, 90–97. Berlin, Heidelberg: Springer Berlin Heidelberg ...