Show simple item record

dc.contributor.authorLee, Seokho
dc.contributor.authorHuang, Jianhua Z.
dc.date.accessioned2016-02-25T12:28:50Z
dc.date.available2016-02-25T12:28:50Z
dc.date.issued2013-06
dc.identifier.citationLee 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.
dc.identifier.issn0167-9473
dc.identifier.doi10.1016/j.csda.2013.01.001
dc.identifier.urihttp://hdl.handle.net/10754/597245
dc.description.abstractSparse 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.
dc.description.sponsorshipThe 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).
dc.publisherElsevier BV
dc.subjectBinary data
dc.subjectCoordinate descent algorithm
dc.subjectMM algorithm
dc.subjectPenalized maximum likelihood
dc.subjectPrincipal component analysis
dc.titleA coordinate descent MM algorithm for fast computation of sparse logistic PCA
dc.typeArticle
dc.identifier.journalComputational Statistics & Data Analysis
dc.contributor.institutionHankuk University of Foreign Studies, Seoul, South Korea
dc.contributor.institutionTexas A and M University, College Station, United States
kaust.grant.numberKUS-CI-016-04


This item appears in the following Collection(s)

Show simple item record