Illumination and reflectance spectra separation of a hyperspectral image meets low-rank matrix factorization

2015 ◽  
Author(s):  
Yinqiang Zheng ◽  
Imari Sato ◽  
Yoichi Sato
Keyword(s):  
Matrix Factorization ◽  
Hyperspectral Image ◽  
Reflectance Spectra ◽  
Low Rank ◽  
Rank Matrix ◽  
Low Rank Matrix

Denoising of Hyperspectral Image Using Low-Rank Matrix Factorization

2017 ◽  
Vol 14 (7) ◽  
pp. 1141-1145 ◽  
Author(s):  
Fei Xu ◽  
Yongyong Chen ◽  
Chong Peng ◽  
Yongli Wang ◽  
Xuefeng Liu ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Hyperspectral Image ◽  
Low Rank ◽  
Rank Matrix ◽  
Low Rank Matrix

Hyperspectral image denoising with bilinear low rank matrix factorization

2019 ◽  
Vol 163 ◽  
pp. 132-152 ◽  
Author(s):  
Huixin Fan ◽  
Jie Li ◽  
Qiangqiang Yuan ◽  
Xinxin Liu ◽  
Michael Ng
Keyword(s):  
Image Denoising ◽  
Matrix Factorization ◽  
Hyperspectral Image ◽  
Low Rank ◽  
Rank Matrix ◽  
Low Rank Matrix

Semi-Orthogonal Low-Rank Matrix Factorization for Deep Neural Networks

2018 ◽  
Author(s):  
Daniel Povey ◽  
Gaofeng Cheng ◽  
Yiming Wang ◽  
Ke Li ◽  
Hainan Xu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Matrix Factorization ◽  
Deep Neural Networks ◽  
Low Rank ◽  
Rank Matrix ◽  
Low Rank Matrix

scholarly journals Single-cell data clustering based on sparse optimization and low-rank matrix factorization

2021 ◽  
Author(s):  
Yinlei Hu ◽  
Bin Li ◽  
Falai Chen ◽  
Kun Qu
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Matrix Factorization ◽  
Data Clustering ◽  
Cell Types ◽  
Low Rank ◽  
Sequencing Data ◽  
Rank Matrix ◽  
Single Cell Rna Sequencing ◽  
Low Rank Matrix

Abstract Unsupervised clustering is a fundamental step of single-cell RNA sequencing data analysis. This issue has inspired several clustering methods to classify cells in single-cell RNA sequencing data. However, accurate prediction of the cell clusters remains a substantial challenge. In this study, we propose a new algorithm for single-cell RNA sequencing data clustering based on Sparse Optimization and low-rank matrix factorization (scSO). We applied our scSO algorithm to analyze multiple benchmark datasets and showed that the cluster number predicted by scSO was close to the number of reference cell types and that most cells were correctly classified. Our scSO algorithm is available at https://github.com/QuKunLab/scSO. Overall, this study demonstrates a potent cell clustering approach that can help researchers distinguish cell types in single-cell RNA sequencing data.

Structural Identifiability in Low-Rank Matrix Factorization

Algorithmica ◽  
2009 ◽  
Vol 56 (3) ◽  
pp. 313-332 ◽  
Author(s):  
Epameinondas Fritzilas ◽  
Martin Milanič ◽  
Sven Rahmann ◽  
Yasmin A. Rios-Solis
Keyword(s):  
Matrix Factorization ◽  
Low Rank ◽  
Structural Identifiability ◽  
Rank Matrix ◽  
Low Rank Matrix
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