Sparse linear discriminant analysis for multiview structured data

Classification methods that leverage the strengths of data from multiple sources (multiview data) simultaneously have enormous potential to yield more powerful findings than two‐step methods: association followed by classification. We propose two methods, sparse integrative discriminant analysis (SI...

Full description

Saved in:
Bibliographic Details
Published in:Biometrics 2022-06, Vol.78 (2), p.612-623
Main Authors: Safo, Sandra E., Min, Eun Jeong, Haine, Lillian
Format: Article
Language:English
Subjects:
Arteriosclerosis
Atherosclerosis
canonical correlation analysis
Cardiovascular diseases
Classification
Correlation analysis
Discriminant Analysis
Humans
integrative analysis
joint association and classification
Laplacian
multiple sources of data
pathway analysis
Risk analysis
Risk factors
sparsity
Structured data
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Classification methods that leverage the strengths of data from multiple sources (multiview data) simultaneously have enormous potential to yield more powerful findings than two‐step methods: association followed by classification. We propose two methods, sparse integrative discriminant analysis (SIDA), and SIDA with incorporation of network information (SIDANet), for joint association and classification studies. The methods consider the overall association between multiview data, and the separation within each view in choosing discriminant vectors that are associated and optimally separate subjects into different classes. SIDANet is among the first methods to incorporate prior structural information in joint association and classification studies. It uses the normalized Laplacian of a graph to smooth coefficients of predictor variables, thus encouraging selection of predictors that are connected. We demonstrate the effectiveness of our methods on a set of synthetic datasets and explore their use in identifying potential nontraditional risk factors that discriminate healthy patients at low versus high risk for developing atherosclerosis cardiovascular disease in 10 years. Our findings underscore the benefit of joint association and classification methods if the goal is to correlate multiview data and to perform classification.
ISSN:0006-341X
1541-0420
DOI:10.1111/biom.13458