Structured Matrix Completion with Applications to Genomic Data Integration.

@article{Cai2015StructuredMC,
  title={Structured Matrix Completion with Applications to Genomic Data Integration.},
  author={Tianxi Cai and Tengfei Cai and Anru Zhang},
  journal={Journal of the American Statistical Association},
  year={2015},
  volume={111 514},
  pages={
          621-633
        }
}
  • Tianxi Cai, Tengfei Cai, Anru Zhang
  • Published 2015
  • Mathematics, Medicine
  • Journal of the American Statistical Association
  • 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… CONTINUE READING

    Figures, Tables, and Topics from this paper.

    Citations

    Publications citing this paper.
    SHOWING 1-10 OF 17 CITATIONS

    Structured Matrix Completion with Applications to Genomic Data Integration.

    VIEW 1 EXCERPT
    CITES BACKGROUND

    Matrix Completion under Low-Rank Missing Mechanism

    VIEW 3 EXCERPTS
    CITES BACKGROUND

    A Novel Sparse Compositional Technique Reveals Microbial Perturbations

    VIEW 1 EXCERPT
    CITES METHODS

    A pan-cancer integrative pathway analysis of multi-omics data

    VIEW 9 EXCERPTS
    CITES METHODS
    HIGHLY INFLUENCED

    Cross: Efficient Low-rank Tensor Completion

    VIEW 9 EXCERPTS
    CITES BACKGROUND & METHODS

    Zero-shot learning with a partial set of observed attributes

    VIEW 13 EXCERPTS
    CITES BACKGROUND & METHODS
    HIGHLY INFLUENCED

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 44 REFERENCES

    Structured Matrix Completion with Applications to Genomic Data Integration.

    VIEW 1 EXCERPT

    Missing value estimation methods for DNA microarrays

    VIEW 1 EXCERPT

    Matrix completion via max-norm constrained optimization

    VIEW 1 EXCERPT

    Uniqueness of Low-Rank Matrix Completion by Rigidity Theory

    VIEW 2 EXCERPTS

    Spectral Regularization Algorithms for Learning Large Incomplete Matrices

    VIEW 5 EXCERPTS
    HIGHLY INFLUENTIAL

    Recovering Low-Rank Matrices From Few Coefficients in Any Basis

    • David Gross
    • Computer Science, Mathematics, Physics
    • IEEE Transactions on Information Theory
    • 2011
    VIEW 2 EXCERPTS

    The Power of Convex Relaxation: Near-Optimal Matrix Completion

    VIEW 2 EXCERPTS