NeNMF: An Optimal Gradient Method for Nonnegative Matrix Factorization

By Naiyang Guan, Dacheng Tao, Zhigang Luo, Bo Yuan. IEEE Transactions on Signal Processing, 2012

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Scalable Temporal Latent Space Inference for Link Prediction in Dynamic Social Networks (2014, unknown): “Nesterov’s gradient method [29], [30], [31] itera-”
A Deep Matrix Factorization Method for Learning Attribute Representations (2015, methodology): “More speciﬁcally, for each of our two datasets we performed the following experiments: • Pixel Intensities: By using only the pixel intensities of the images in each of our datasets, which of course give us a strictly non-negative input data matrix X , we compare the reconstruction error and the clu”
Multi-View Clustering via Deep Matrix Factorization (2017, background): “Recently, lots of research activities on MVC have achieved promising performance based on Non-negative Matrix Factorization (NMF) and its variants, because the non-negativity constraints allow for better interpretability (Guan et al. 2012; Trigeorgis et al. 2014).”
The Why and How of Nonnegative Matrix Factorization (2014, methodology): “Other methods are based for example on projected gradients [88], Quasi-Newton [26], or fast gradient methods [60].”