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    A coordinate descent MM algorithm for fast computation of sparse logistic PCA

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    Type
    Article
    Authors
    Lee, Seokho
    Huang, Jianhua Z.
    KAUST Grant Number
    KUS-CI-016-04
    Date
    2013-06
    Permanent link to this record
    http://hdl.handle.net/10754/597245
    
    Metadata
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    Abstract
    Sparse logistic principal component analysis was proposed in Lee et al. (2010) for exploratory analysis of binary data. Relying on the joint estimation of multiple principal components, the algorithm therein is computationally too demanding to be useful when the data dimension is high. We develop a computationally fast algorithm using a combination of coordinate descent and majorization-minimization (MM) auxiliary optimization. Our new algorithm decouples the joint estimation of multiple components into separate estimations and consists of closed-form elementwise updating formulas for each sparse principal component. The performance of the proposed algorithm is tested using simulation and high-dimensional real-world datasets. © 2013 Elsevier B.V. All rights reserved.
    Citation
    Lee S, Huang JZ (2013) A coordinate descent MM algorithm for fast computation of sparse logistic PCA. Computational Statistics & Data Analysis 62: 26–38. Available: http://dx.doi.org/10.1016/j.csda.2013.01.001.
    Sponsors
    The authors would like to thank the Editor, the Associate Editor and reviewers for helpful comments. Lee’s work was supported by Basic Science Research Program through the National Research Foundation (NRF) of Korea (2011-0011608). Huang’s work was partially supported by grants from NCI (CA57030), NSF (DMS-0907170, DMS-1007618, DMS-1208952), and King Abdullah University of Science and Technology (KUS-CI-016-04).
    Publisher
    Elsevier BV
    Journal
    Computational Statistics & Data Analysis
    DOI
    10.1016/j.csda.2013.01.001
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.csda.2013.01.001
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