Nonnegative Matrix Factorization (NMF) is a key tool for model dimensionality reduction in supervised classification. Several NMF algorithms have been developed for this purpose. In a majority of them, the training process is improved by using discriminant or nearest-neighbor graph-based constraints that are obtained from the knowledge on class labels of training samples. The constraints are usually incorporated to NMF algorithms by l2-weighted penalty terms that involve formulating a large-size weighting matrix. Using the Newton method for updating the latent factors, the optimization problems in NMF become large-scale. However, the computational problem can be considerably alleviated if the modified Spectral Projected Gradient (SPG) that belongs to a class of quasi-Newton methods is used. The simulation results presented for the selected classification problems demonstrate the high efficiency of the proposed method.
CITATION STYLE
Zdunek, R., Phan, A. H., & Cichocki, A. (2015). Image classification with nonnegative matrix factorization based on spectral projected gradient. In Artificial Neural Networks - Methods and Applications in Bio-/Neuroinformatics (pp. 31–50). Springer Verlag. https://doi.org/10.1007/978-3-319-09903-3_2
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