Sparse robust graph-regularized non-negative matrix factorization based on correntropy
Author:
Affiliation:
1. School of Computer Science, Qufu Normal University, Rizhao, Shandong, P. R. China
2. Qufu Normal University Library, Qufu Normal University, Rizhao, Shandong, P. R. China
Abstract
Funder
National Natural Science Foundation of China
Publisher
World Scientific Pub Co Pte Lt
Subject
Computer Science Applications,Molecular Biology,Biochemistry
Link
https://www.worldscientific.com/doi/pdf/10.1142/S021972002050047X
Reference45 articles.
1. Regularized Non-Negative Matrix Factorization for Identifying Differentially Expressed Genes and Clustering Samples: A Survey
2. Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis
3. An NMF-L2,1-Norm Constraint Method for Characteristic Gene Selection
4. Graph Regularized Nonnegative Matrix Factorization for Data Representation
5. Non-negative matrix factorization by maximizing correntropy for cancer clustering
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2. Learning transferable non-negative feature representation for facial expression recognition;Digital Signal Processing;2023-07
3. Sparse Hyper-graph Non-negative Matrix Factorization by Maximizing Correntropy;2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM);2021-12-09
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