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

Handle URI:
http://hdl.handle.net/10754/597245
Title:
A coordinate descent MM algorithm for fast computation of sparse logistic PCA
Authors:
Lee, Seokho; Huang, Jianhua Z.
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.
Publisher:
Elsevier BV
Journal:
Computational Statistics & Data Analysis
KAUST Grant Number:
KUS-CI-016-04
Issue Date:
Jun-2013
DOI:
10.1016/j.csda.2013.01.001
Type:
Article
ISSN:
0167-9473
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).
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Full metadata record

DC FieldValue Language
dc.contributor.authorLee, Seokhoen
dc.contributor.authorHuang, Jianhua Z.en
dc.date.accessioned2016-02-25T12:28:50Zen
dc.date.available2016-02-25T12:28:50Zen
dc.date.issued2013-06en
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.en
dc.identifier.issn0167-9473en
dc.identifier.doi10.1016/j.csda.2013.01.001en
dc.identifier.urihttp://hdl.handle.net/10754/597245en
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.en
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).en
dc.publisherElsevier BVen
dc.subjectBinary dataen
dc.subjectCoordinate descent algorithmen
dc.subjectMM algorithmen
dc.subjectPenalized maximum likelihooden
dc.subjectPrincipal component analysisen
dc.titleA coordinate descent MM algorithm for fast computation of sparse logistic PCAen
dc.typeArticleen
dc.identifier.journalComputational Statistics & Data Analysisen
dc.contributor.institutionHankuk University of Foreign Studies, Seoul, South Koreaen
dc.contributor.institutionTexas A and M University, College Station, United Statesen
kaust.grant.numberKUS-CI-016-04en
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