Hyperspectral image restoration using framelet-regularized low-rank nonnegative matrix factorization

2018 ◽  
Vol 63 ◽  
pp. 128-147 ◽  
Author(s):  
Yong Chen ◽  
Ting-Zhu Huang ◽  
Xi-Le Zhao ◽  
Liang-Jian Deng
Keyword(s):  
Image Restoration ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Hyperspectral Image ◽  
Nonnegative Matrix ◽  
Low Rank

scholarly journals An Accelerated Symmetric Nonnegative Matrix Factorization Algorithm Using Extrapolation

Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1187
Author(s):  
Peitao Wang ◽  
Zhaoshui He ◽  
Jun Lu ◽  
Beihai Tan ◽  
YuLei Bai ◽  
...  
Keyword(s):  
Real World ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Low Rank ◽  
Tensor Factorization ◽  
Real World Data ◽  
Restart Strategy ◽  
Extrapolation Scheme ◽  
Symmetric Nonnegative Matrix Factorization

Symmetric nonnegative matrix factorization (SNMF) approximates a symmetric nonnegative matrix by the product of a nonnegative low-rank matrix and its transpose. SNMF has been successfully used in many real-world applications such as clustering. In this paper, we propose an accelerated variant of the multiplicative update (MU) algorithm of He et al. designed to solve the SNMF problem. The accelerated algorithm is derived by using the extrapolation scheme of Nesterov and a restart strategy. The extrapolation scheme plays a leading role in accelerating the MU algorithm of He et al. and the restart strategy ensures that the objective function of SNMF is monotonically decreasing. We apply the accelerated algorithm to clustering problems and symmetric nonnegative tensor factorization (SNTF). The experiment results on both synthetic and real-world data show that it is more than four times faster than the MU algorithm of He et al. and performs favorably compared to recent state-of-the-art algorithms.

A hybrid algorithm for low-rank approximation of nonnegative matrix factorization

2019 ◽  
Vol 364 ◽  
pp. 129-137
Author(s):  
Peitao Wang ◽  
Zhaoshui He ◽  
Kan Xie ◽  
Junbin Gao ◽  
Michael Antolovich ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Hybrid Algorithm ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Low Rank ◽  
Low Rank Approximation ◽  
Rank Approximation

A Review of the Hyperspectral Unmixing Methods that Based on Constrained NMF and Constrained Sparse Regression

2015 ◽  
Vol 713-715 ◽  
pp. 1540-1545
Author(s):  
Cheng Yong Zheng
Keyword(s):  
Image Analysis ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Hyperspectral Image ◽  
Nonnegative Matrix ◽  
The Other ◽  
Research Topics ◽  
Sparse Regression ◽  
Hyperspectral Unmixing ◽  
Hyperspectral Image Analysis

Hyperspectral unmixing (HSU) plays an important role in hyperspectral image analysis, and most of the current HSU algorithms are base on linear mixing model (LMM). This paper gives a review of two linear HSU methods that have been drawn great attention recently: one is constrained nonnegative matrix factorization (CNMF) based method, the other is constrained sparse regression (CSR) based method. We carried on the systematic summary to these two types of methods, based on which, we point out some potential research topics.

Hyperspectral image unmixing algorithm based on endmember-constrained nonnegative matrix factorization

2016 ◽  
Vol 9 (4) ◽  
pp. 627-632 ◽  
Author(s):  
Yan Zhao ◽  
Zhen Zhou ◽  
Donghui Wang ◽  
Yicheng Huang ◽  
Minghua Yu
Keyword(s):  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Hyperspectral Image ◽  
Nonnegative Matrix

Hyperspectral Image Unmixing Based on Constrained Nonnegative Matrix Factorization

2019 ◽  
Vol 56 (16) ◽  
pp. 161001
Author(s):  
方帅 Shuai Fang ◽  
王金明 Jinming Wang ◽  
曹风云 Fengyun Cao
Keyword(s):  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Hyperspectral Image ◽  
Nonnegative Matrix
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