Non-negative matrix factorization by maximizing correntropy for cancer clustering
KAUST DepartmentComputational Bioscience Research Center (CBRC)
Computer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Structural and Functional Bioinformatics Group
Online Publication Date2013-03-24
Print Publication Date2013
Permanent link to this recordhttp://hdl.handle.net/10754/325473
MetadataShow full item record
AbstractBackground: Non-negative matrix factorization (NMF) has been shown to be a powerful tool for clustering gene expression data, which are widely used to classify cancers. NMF aims to find two non-negative matrices whose product closely approximates the original matrix. Traditional NMF methods minimize either the l2 norm or the Kullback-Leibler distance between the product of the two matrices and the original matrix. Correntropy was recently shown to be an effective similarity measurement due to its stability to outliers or noise.Results: We propose a maximum correntropy criterion (MCC)-based NMF method (NMF-MCC) for gene expression data-based cancer clustering. Instead of minimizing the l2 norm or the Kullback-Leibler distance, NMF-MCC maximizes the correntropy between the product of the two matrices and the original matrix. The optimization problem can be solved by an expectation conditional maximization algorithm.Conclusions: Extensive experiments on six cancer benchmark sets demonstrate that the proposed method is significantly more accurate than the state-of-the-art methods in cancer clustering. 2013 Wang et al.; licensee BioMed Central Ltd.
CitationWang JJ-Y, Wang X, Gao X (2013) Non-negative matrix factorization by maximizing correntropy for cancer clustering. BMC Bioinformatics 14: 107. doi:10.1186/1471-2105-14-107.
PubMed Central IDPMC3659102
The following license files are associated with this item:
Except where otherwise noted, this item's license is described as This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- Hessian regularization based non-negative matrix factorization for gene expression data clustering.
- Authors: Liu X, Shi J, Wang C
- Issue date: 2015
- A Robust Manifold Graph Regularized Nonnegative Matrix Factorization Algorithm for Cancer Gene Clustering.
- Authors: Zhu R, Liu JX, Zhang YK, Guo Y
- Issue date: 2017 Dec 2
- Correntropy-Based Hypergraph Regularized NMF for Clustering and Feature Selection on Multi-Cancer Integrated Data.
- Authors: Yu N, Wu MJ, Liu JX, Zheng CH, Xu Y
- Issue date: 2020 Jun 30
- Hessian regularization based symmetric nonnegative matrix factorization for clustering gene expression and microbiome data.
- Authors: Ma Y, Hu X, He T, Jiang X
- Issue date: 2016 Dec 1
- Robust Bi-stochastic Graph Regularized Matrix Factorization for Data Clustering.
- Authors: Wang Q, He X, Jiang X, Li X
- Issue date: 2020 Jul 7