Robust hypergraph regularized non-negative matrix factorization for sample clustering and feature selection in multi-view gene expression data.

ABSTRACT: BACKGROUND:As one of the most popular data representation methods, non-negative matrix decomposition (NMF) has been widely concerned in the tasks of clustering and feature selection. However, most of the previously proposed NMF-based methods do not adequately explore the hidden geometrical structure in the data. At the same time, noise and outliers are inevitably present in the data. RESULTS:To alleviate these problems, we present a novel NMF framework named robust hypergraph regularized non-negative matrix factorization (RHNMF). In particular, the hypergraph Laplacian regularization is imposed to capture the geometric information of original data. Unlike graph Laplacian regularization which captures the relationship between pairwise sample points, it captures the high-order relationship a

SUBMITTER: Yu N 

PROVIDER: S-EPMC6805321 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature