A method for learning a sparse classifier in the presence of missing data for high-dimensional biological datasets
Abstract
This work addresses two common issues in building classification models for biological or medical studies: learning a sparse model, where only a subset of a large number of possible predictors is used, and training in the presence of missing data. This work focuses on supervised generative binary classification models, specifically linear discriminant analysis (LDA). The parameters are determined using an expectation maximization algorithm to both address missing data and introduce priors to promote sparsity. The proposed algorithm, expectation-maximization sparse discriminant analysis (EM-SDA), produces a sparse LDA model for datasets with and without missing data.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Apr 18, 2017
- Source ID
- 10.1093/bioinformatics/btx224
Entities
People
- Brinda Monian
- J Christopher Love
- Kristen A. Severson
- Richard D. Braatz
Organizations
- Army Research Office
- Defense Advanced Research Projects Agency
- Massachusetts Institute of Technology
- United States Army Medical Research and Development Command
- United States Department of Defense