TY - GEN
T1 - Hypergraph regularized NMF by L2,1-norm for Clustering and Com-abnormal Expression Genes Selection
AU - Yu, Na
AU - Gao, Ying Lian
AU - Liu, Jin Xing
AU - Wang, Juan
AU - Shang, Junliang
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2019/1/21
Y1 - 2019/1/21
N2 - Non-negative matrix decomposition (NMF) has been widely used for sample clustering and feature selection in the field of bioinformatics. However, the existing methods based on NMF cannot effectively deal with the problem of intrinsic geometrical structure, noise, and outliers in gene expression data. In this paper, a novel method called Robust Hypergraph regularized Non-negative Matrix Factorization (RHNMF) is proposed to solve the above problem. Firstly, the hypergraph Laplacian regularization is introduced to consider the intrinsic geometrical structure of the high dimension data. Secondly, the L2,1-norm is applied in the error function to reduce effects of the noise and outliers, which may improve the robustness of the algorithm. Finally, we perform clustering and common abnormal expression genes (com-abnormal expression genes) selection on multi-view gene expression data to verify the rationality and validity of the RHNMF method. Extensive experimental results demonstrate that our proposed RHNMF method has better performance than other state-of-the-art methods.
AB - Non-negative matrix decomposition (NMF) has been widely used for sample clustering and feature selection in the field of bioinformatics. However, the existing methods based on NMF cannot effectively deal with the problem of intrinsic geometrical structure, noise, and outliers in gene expression data. In this paper, a novel method called Robust Hypergraph regularized Non-negative Matrix Factorization (RHNMF) is proposed to solve the above problem. Firstly, the hypergraph Laplacian regularization is introduced to consider the intrinsic geometrical structure of the high dimension data. Secondly, the L2,1-norm is applied in the error function to reduce effects of the noise and outliers, which may improve the robustness of the algorithm. Finally, we perform clustering and common abnormal expression genes (com-abnormal expression genes) selection on multi-view gene expression data to verify the rationality and validity of the RHNMF method. Extensive experimental results demonstrate that our proposed RHNMF method has better performance than other state-of-the-art methods.
KW - Clustering
KW - Common Abnormal Genes Selection
KW - Hypergraph Laplacian
KW - L2,1-norm
KW - Multi-view Gene Expression Data
KW - Non-negative Matrix Decomposition
UR - https://www.scopus.com/pages/publications/85062514420
U2 - 10.1109/BIBM.2018.8621454
DO - 10.1109/BIBM.2018.8621454
M3 - 会议稿件
AN - SCOPUS:85062514420
T3 - Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
SP - 578
EP - 582
BT - Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
A2 - Schmidt, Harald
A2 - Griol, David
A2 - Wang, Haiying
A2 - Baumbach, Jan
A2 - Zheng, Huiru
A2 - Callejas, Zoraida
A2 - Hu, Xiaohua
A2 - Dickerson, Julie
A2 - Zhang, Le
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
Y2 - 3 December 2018 through 6 December 2018
ER -