Chun-Mei Feng

According to our database1, Chun-Mei Feng authored at least 11 papers between 2016 and 2019.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Other 

Links

On csauthors.net:

Bibliography

2019
Supervised Discriminative Sparse PCA for Com-Characteristic Gene Selection and Tumor Classification on Multiview Biological Data.
IEEE Trans. Neural Networks Learn. Syst., 2019

Coupled-Projection Residual Network for MRI Super-Resolution.
CoRR, 2019

Robust Classification with Sparse Representation Fusion on Diverse Data Subsets.
CoRR, 2019

PCA via joint graph Laplacian and sparse constraint: Identification of differentially expressed genes and sample clustering on gene expression data.
BMC Bioinform., 2019

Dual Graph-Laplacian PCA: A Closed-Form Solution for Bi-Clustering to Find "Checkerboard" Structures on Gene Expression Data.
IEEE Access, 2019

2017
Robust Nonnegative Matrix Factorization via Joint Graph Laplacian and Discriminative Information for Identifying Differentially Expressed Genes.
Complexity, 2017

Feature selection and clustering via robust graph-laplacian PCA based on capped L1-norm.
Proceedings of the 2017 IEEE International Conference on Bioinformatics and Biomedicine, 2017

Robust graph regularized sparse orthogonal nonnegative matrix factorization for identifying differentially expressed genes.
Proceedings of the 2017 IEEE International Conference on Bioinformatics and Biomedicine, 2017

2016
A Simple Review of Sparse Principal Components Analysis.
Proceedings of the Intelligent Computing Theories and Application, 2016

A graph-Laplacian PCA based on L1/2-norm constraint for characteristic gene selection.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2016

Robust graph regularized discriminative nonnegative matrix factorization for characteristic gene selection.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2016


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