Sparse logistic principal components analysis for binary data

Details Sparse logistic principal components analysis for binary data Sparse logistic principal components analysis for binary data

2010-11-16

arxiv.org/abs/1011.3626v1

Annals of Applied Statistics 2010, Vol. 4, No. 3, 1579-1601

Statistics

Applications

Published in at http://dx.doi.org/10.1214/10-AOAS327 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Ins

Scientific paper

10.1214/10-AOAS327

We develop a new principal components analysis (PCA) type dimension reduction method for binary data. Different from the standard PCA which is defined on the observed data, the proposed PCA is defined on the logit transform of the success probabilities of the binary observations. Sparsity is introduced to the principal component (PC) loading vectors for enhanced interpretability and more stable extraction of the principal components. Our sparse PCA is formulated as solving an optimization problem with a criterion function motivated from a penalized Bernoulli likelihood. A Majorization--Minimization algorithm is developed to efficiently solve the optimization problem. The effectiveness of the proposed sparse logistic PCA method is illustrated by application to a single nucleotide polymorphism data set and a simulation study.

Affiliated with

Also associated with

No associations

Rating

Sparse logistic principal components analysis for binary data does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Sparse logistic principal components analysis for binary data, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Sparse logistic principal components analysis for binary data will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-464060