Structured Matrix Completion with Applications to Genomic Data Integration.

ABSTRACT: Matrix completion has attracted significant recent attention in many fields including statistics, applied mathematics and electrical engineering. Current literature on matrix completion focuses primarily on independent sampling models under which the individual observed entries are sampled independently. Motivated by applications in genomic data integration, we propose a new framework of structured matrix completion (SMC) to treat structured missingness by design. Specifically, our proposed method aims at efficient matrix recovery when a subset of the rows and columns of an approximately low-rank matrix are observed. We provide theoretical justification for the proposed SMC method and derive lower bound for the estimation errors, which together establish the optimal rate of recovery over c

SUBMITTER: Cai T 

PROVIDER: S-EPMC5198844 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature