scholarly journals FPGA Implementation of L1/2 Sparsity Constrained Nonnegative Matrix Factorization Algorithm for Remotely Sensed Hyperspectral Image Analysis

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 12069-12083
Author(s):  
Mostafa Guda ◽  
Safa Gasser ◽  
Mohamed S. El-Mahallawy ◽  
Khaled Shehata
Keyword(s):  
Image Analysis ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Hyperspectral Image ◽  
Nonnegative Matrix ◽  
Remotely Sensed ◽  
Fpga Implementation ◽  
Hyperspectral Image Analysis ◽  
Factorization Algorithm

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.

The application of semi-nonnegative matrix factorization for detection of incipient faults in bearings

2019 ◽  
Vol 233 (13) ◽  
pp. 4543-4555 ◽  
Author(s):  
Akhand Rai ◽  
Sanjay H Upadhyay
Keyword(s):  
Frequency Domain ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Health Indicators ◽  
Data Matrix ◽  
Bearing Faults ◽  
Time Frequency ◽  
Factorization Algorithm ◽  
Incipient Faults

Bearing faults are a major reason for the catastrophic breakdown of rotating machinery. Therefore, the early detection of bearing faults becomes a necessity to attain an uninterrupted and safe operation. This paper proposes a novel approach based on semi-nonnegative matrix factorization for detection of incipient faults in bearings. The semi-nonnegative matrix factorization algorithm creates a sparse, localized, part-based representation of the original data and assists to capture the fault information in bearing signals more effectively. Through semi-nonnegative matrix factorization, two bearing health indicators are derived to fulfill the desired purpose. In doing so, the paper tries to address two critical issues: (i) how to reduce the dimensionality of feature space (ii) how to obtain a definite range of the indicator between 0 and 1. Firstly, a set of time domain, frequency domain, and time–frequency domain features are extracted from the bearing vibration signals. Secondly, the feature dataset is utilized to train the semi-nonnegative matrix factorization algorithm which decomposes the training data matrix into two new matrices of lower ranks. Thirdly, the test feature vectors are projected onto these lower dimensional matrices to obtain two statistics called as square prediction error and Q2. Finally, the Bayesian inference approach is exploited to convert the two statistics into health indicators that have a fixed range between [0–1]. The application of the advocated technique on experimental bearing signals demonstrates that it can effectively predict the weak defects in bearings as well as performs better than the earlier methods like principal component analysis and locality preserving projections.

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
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