Ying-Lian Gao

TL;DR: This survey paper mainly focuses on research examining the application of NMF to identify differentially expressed genes and to cluster samples, and the main NMF models, properties, principles, and algorithms with its various generalizations, extensions, and modifications are summarized.

TL;DR: A novel PCA method enforcing P-norm on error function and graph-Laplacian regularization term for matrix decomposition problem, called as PgLPCA, which has higher accuracy than compared methods and an efficient optimization algorithm based on the augmented Lagrange multiplier method.

TL;DR: The main innovation of this method is the incorporation of discriminative information and sparsity into the PCA model, and it is demonstrated that SDSPCA outperforms other state-of-the-art methods.

TL;DR: A novel matrix decomposition method called Hyper-graph regularized Constrained Non-negative Matrix Factorization (HCNMF) is proposed for selecting differentially expressed genes and tumor sample classification and the application of hyper-graph theory can effectively find pathogenic genes in cancer datasets.

TL;DR: A novel method named robust graph regularized non-negative matrix factorization for characteristic gene selection using gene expression data, which mainly contains enforcing L21-norm minimization on error function which is robust to outliers and noises in data points.