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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. The main function in our package, smc.FUN, is for recovery of the missing block A22 of an approximately low-rank matrix A given the other blocks A11, A12, A21.
This is the R package `StructureMC’ (Structured Matrix Completion) made Yifu Liu(2012johnnyliu@gmail.com) and Anru Zhang(anruzhang@stat.wisc.edu) and maintained by Yifu Liu(2012johnnyliu@gmail.com).
For more information about the Structured Matrix Completion, please refer to the paper “Cai, T., Cai, T. T., & Zhang, A. (2015). Structured Matrix Completion with Applications to Genomic Data Integration. Journal of the American Statistical Association.”
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