Semi-supervised Classification Using Multiple Clustering and Low-Rank Matrix Operations

2019 ◽  
pp. 529-540
Author(s):  
Vladimir Berikov
Keyword(s):  
Supervised Classification ◽  
Low Rank ◽  
Matrix Operations ◽  
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

Adaptive Local Low-rank Matrix Approximation for Recommendation

2019 ◽  
Vol 37 (4) ◽  
pp. 1-34 ◽  
Author(s):  
Huafeng Liu ◽  
Liping Jing ◽  
Yuhua Qian ◽  
Jian Yu
Keyword(s):  
Low Rank ◽  
Matrix Approximation ◽  
Rank Matrix ◽  
Low Rank Matrix ◽  
Low Rank Matrix Approximation

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.

scholarly journals Low-Rank Matrix Recovery with Composite Optimization: Good Conditioning and Rapid Convergence

2021 ◽  
Author(s):  
Vasileios Charisopoulos ◽  
Yudong Chen ◽  
Damek Davis ◽  
Mateo Díaz ◽  
Lijun Ding ◽  
...  
Keyword(s):  
Low Rank ◽  
Rapid Convergence ◽  
Composite Optimization ◽  
Matrix Recovery ◽  
Rank Matrix ◽  
Low Rank Matrix Recovery ◽  
Low Rank Matrix ◽  
Good Conditioning
Sign in / Sign up

Export Citation Format

Share Document