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

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.

author list (cited authors)

• Lee, S., & Huang, J. Z.

citation count

• 5

complete list of authors

• Lee, Seokho||Huang, Jianhua Z

publication date

• June 2013

published in

• Computational Statistics and Data Analysis  Journal

keywords

• Binary Data
• Coordinate Descent Algorithm
• Mm Algorithm
• Penalized Maximum Likelihood
• Principal Component Analysis

Digital Object Identifier (DOI)

• 10.1016/j.csda.2013.01.001

start page

• 26

end page

• 38

volume

• 62