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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 26-38 |
| Number of pages | 13 |
| Journal | Computational Statistics & Data Analysis |
| Volume | 62 |
| DOIs | |
| State | Published - Jun 2013 |
| Externally published | Yes |
Bibliographical note
KAUST Repository Item: Exported on 2020-10-01Acknowledged KAUST grant number(s): KUS-CI-016-04
Acknowledgements: 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).
This publication acknowledges KAUST support, but has no KAUST affiliated authors.