A non-negative matrix factorization method for bilinear-bilinear unmixing of hyperspectral images

2013 ◽  
Author(s):  
Olivier Eches ◽  
Mireille Guillaume
Keyword(s):  
Matrix Factorization ◽  
Factorization Method ◽  
Hyperspectral Images ◽  
Non Negative Matrix Factorization

scholarly journals HyTexiLa: High Resolution Visible and Near Infrared Hyperspectral Texture Images

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2045 ◽  
Author(s):  
Haris Khan ◽  
Sofiane Mihoubi ◽  
Benjamin Mathon ◽  
Jean-Baptiste Thomas ◽  
Jon Hardeberg
Keyword(s):  
Spectral Analysis ◽  
High Resolution ◽  
Matrix Factorization ◽  
Local Binary Pattern ◽  
Near Infrared ◽  
Texture Classification ◽  
Hyperspectral Images ◽  
Close Range ◽  
Local Binary Pattern Feature ◽  
Non Negative Matrix Factorization

We present a dataset of close range hyperspectral images of materials that span the visible and near infrared spectrums: HyTexiLa (Hyperspectral Texture images acquired in Laboratory). The data is intended to provide high spectral and spatial resolution reflectance images of 112 materials to study spatial and spectral textures. In this paper we discuss the calibration of the data and the method for addressing the distortions during image acquisition. We provide a spectral analysis based on non-negative matrix factorization to quantify the spectral complexity of the samples and extend local binary pattern operators to the hyperspectral texture analysis. The results demonstrate that although the spectral complexity of each of the textures is generally low, increasing the number of bands permits better texture classification, with the opponent band local binary pattern feature giving the best performance.

An Improved Non-negative Matrix Factorization Method for Dynamic Industrial Fault Diagnosis

2020 ◽  
Vol 53 (7) ◽  
pp. 321-326
Author(s):  
XiaoFeng Gong ◽  
Dongdong Sun ◽  
Zuodong Tang ◽  
Kai Zhou ◽  
RuiSen Luo
Keyword(s):  
Fault Diagnosis ◽  
Matrix Factorization ◽  
Factorization Method ◽  
Non Negative Matrix Factorization

scholarly journals Indicator Regularized Non-Negative Matrix Factorization Method-Based Drug Repurposing for COVID-19

2021 ◽  
Vol 11 ◽  
Author(s):  
Xianfang Tang ◽  
Lijun Cai ◽  
Yajie Meng ◽  
JunLin Xu ◽  
Changcheng Lu ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
De Novo ◽  
Characteristic Curve ◽  
Drug Repurposing ◽  
Factorization Method ◽  
Therapeutic Drugs ◽  
Indicator Matrix ◽  
Novel Coronavirus ◽  
Approved Drugs ◽  
Non Negative Matrix Factorization

A novel coronavirus, named COVID-19, has become one of the most prevalent and severe infectious diseases in human history. Currently, there are only very few vaccines and therapeutic drugs against COVID-19, and their efficacies are yet to be tested. Drug repurposing aims to explore new applications of approved drugs, which can significantly reduce time and cost compared with de novo drug discovery. In this study, we built a virus-drug dataset, which included 34 viruses, 210 drugs, and 437 confirmed related virus-drug pairs from existing literature. Besides, we developed an Indicator Regularized non-negative Matrix Factorization (IRNMF) method, which introduced the indicator matrix and Karush-Kuhn-Tucker condition into the non-negative matrix factorization algorithm. According to the 5-fold cross-validation on the virus-drug dataset, the performance of IRNMF was better than other methods, and its Area Under receiver operating characteristic Curve (AUC) value was 0.8127. Additionally, we analyzed the case on COVID-19 infection, and our results suggested that the IRNMF algorithm could prioritize unknown virus-drug associations.

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