Jin-Xing Liu

TL;DR: A novel method called correntropy-based hypergraph regularized NMF (CHNMF) is proposed to solve the complex optimization problem of non-negative matrix factorization and extensive experimental results indicate that the proposed method is superior to other state-of-the-art methods for clustering and feature selection.

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: Highlights of epiACO are the introduced fitness function Svalue, path selection strategies, and a memory based strategy that are designed to retain candidate solutions found in the previous iterations and compare them to solutions of the current iteration to generate new candidate solutions, yielding a more accurate way for identifying epistasis.

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.