SOFAR: Large-Scale Association Network Learning.

ABSTRACT: Many modern big data applications feature large scale in both numbers of responses and predictors. Better statistical efficiency and scientific insights can be enabled by understanding the large-scale response-predictor association network structures via layers of sparse latent factors ranked by importance. Yet sparsity and orthogonality have been two largely incompatible goals. To accommodate both features, in this paper we suggest the method of sparse orthogonal factor regression (SOFAR) via the sparse singular value decomposition with orthogonality constrained optimization to learn the underlying association networks, with broad applications to both unsupervised and supervised learning tasks such as biclustering with sparse singular value decomposition, sparse principal component analys

SUBMITTER: Uematsu Y 

PROVIDER: S-EPMC7970712 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature