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Matrix completion discriminant analysis

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  • Wu, Tong Tong
  • Lange, Kenneth

Abstract

Matrix completion discriminant analysis (MCDA) is designed for semi-supervised learning where the rate of missingness is high and predictors vastly outnumber cases. MCDA operates by mapping class labels to the vertices of a regular simplex. With c classes, these vertices are arranged on the surface of the unit sphere in c−1 dimensional Euclidean space. Because all pairs of vertices are equidistant, the classes are treated symmetrically. To assign unlabeled cases to classes, the data is entered into a large matrix (cases along rows and predictors along columns) that is augmented by vertex coordinates stored in the last c−1 columns. Once the matrix is constructed, its missing entries can be filled in by matrix completion. To carry out matrix completion, one minimizes a sum of squares plus a nuclear norm penalty. The simplest solution invokes an MM algorithm and singular value decomposition. Choice of the penalty tuning constant can be achieved by cross validation on randomly withheld case labels. Once the matrix is completed, an unlabeled case is assigned to the class vertex closest to the point deposited in its last c−1 columns. A variety of examples drawn from the statistical literature demonstrate that MCDA is competitive on traditional problems and outperforms alternatives on large-scale problems.

Suggested Citation

  • Wu, Tong Tong & Lange, Kenneth, 2015. "Matrix completion discriminant analysis," Computational Statistics & Data Analysis, Elsevier, vol. 92(C), pages 115-125.
  • Handle: RePEc:eee:csdana:v:92:y:2015:i:c:p:115-125
    DOI: 10.1016/j.csda.2015.06.006
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    References listed on IDEAS

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    1. Hunter D.R. & Lange K., 2004. "A Tutorial on MM Algorithms," The American Statistician, American Statistical Association, vol. 58, pages 30-37, February.
    2. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    3. Liu, Yufeng & Zhang, Hao Helen & Wu, Yichao, 2011. "Hard or Soft Classification? Large-Margin Unified Machines," Journal of the American Statistical Association, American Statistical Association, vol. 106(493), pages 166-177.
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