Author of the publication

L2, 1-Extreme Learning Machine: An Efficient Robust Classifier for Tumor Classification.

, , , , and . Comput. Biol. Chem., (2020)

Please choose a person to relate this publication to

To differ between persons with the same name, the academic degree and the title of an important publication will be displayed. You can also use the button next to the name to display some publications already assigned to the person.

 

Other publications of authors with the same name

Laplacian regularized low-rank representation for cancer samples clustering., , , , and . Comput. Biol. Chem., (2019)Multi-cancer samples clustering via graph regularized low-rank representation method under sparse and symmetric constraints., , , , and . BMC Bioinform., 20-S (22): 718 (2019)Dual Graph regularized PCA based on Different Norm Constraints for Bi-clustering Analysis on Single-cell RNA-seq Data., , , , , and . BIBM, page 92-95. IEEE, (2020)Tensor Robust Principal Component Analysis with Low-Rank Weight Constraints for Sample Clustering., , , , , and . BIBM, page 397-401. IEEE, (2020)Locally Manifold Non-negative Matrix Factorization Based on Centroid for scRNA-seq Data Analysis., , , , , and . BIBM, page 121-125. IEEE, (2020)Multi-view manifold regularized compact low-rank representation for cancer samples clustering on multi-omics data., , , , , and . BMC Bioinform., 22-S (12): 334 (January 2022)Hyper-graph Robust Non-negative Matrix Factorization Method for Cancer Sample Clustering and Feature Selection., , , and . IDMB, volume 1099 of Communications in Computer and Information Science, page 112-125. Springer, (2019)Extreme Learning Machine Based on Double Kernel Risk-Sensitive Loss for Cancer Samples Classification., , , , , and . ICIC (2), volume 12837 of Lecture Notes in Computer Science, page 532-539. Springer, (2021)Joint CC and Bimax: A Biclustering Method for Single-Cell RNA-Seq Data Analysis., , , , , and . ISBRA, volume 13064 of Lecture Notes in Computer Science, page 499-510. Springer, (2021)Dual Graph-Laplacian PCA: A Closed-Form Solution for Bi-Clustering to Find "Checkerboard" Structures on Gene Expression Data., , , and . IEEE Access, (2019)