Parallel Implementation of the Nonlinear Semi-NMF Based Alternating Optimization Method for Deep Neural Networks
Sign Up to like & getrecommendations! Published in 2017 at "Neural Processing Letters"
DOI: 10.1007/s11063-017-9642-2
Abstract: For computing weights of deep neural networks (DNNs), the backpropagation (BP) method has been widely used as a de-facto standard algorithm. Since the BP method is based on a stochastic gradient descent method using derivatives… read more here.
Keywords: nmf based; parallel implementation; method; nonlinear semi ... See more keywords
Robust semi-NMF with total variation for unsupervised SAR image change detection
Sign Up to like & getrecommendations! Published in 2018 at "Electronics Letters"
DOI: 10.1049/el.2018.1269
Abstract: A novel unsupervised change detection for synthetic aperture radar (SAR) images using robust semi-non-negative matrix factorisation (NMF) with total variation (TV) is proposed. In order to effectively handle the noise and outliers as well as… read more here.
Keywords: semi; nmf total; change detection; total variation ... See more keywords
Bi-endmember Semi-NMF Based on Low-Rank and Sparse Matrix Decomposition
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2022.3159998
Abstract: This paper presents a bi-endmember semi-nonnegative matrix factorization (Semi-NMF) algorithm based on low-rank and sparse matrix decomposition (LRSMD), referred to as BLSNMF, to resolve the issues of endmember variability and nonlinear mixing. Given the fact… read more here.
Keywords: rank sparse; low rank; semi nmf; decomposition ... See more keywords