Ridge Fusion in Statistical Learning

This article proposes a penalized likelihood method to jointly estimate multiple precision matrices for use in quadratic discriminant analysis (QDA) and model-based clustering. We use a ridge penalty and a ridge fusion penalty to introduce shrinkage and promote similarity between precision matrix es...

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Bibliographic Details
Published in:Journal of computational and graphical statistics 2015-04, Vol.24 (2), p.439-454
Main Authors: Price, Bradley S., Geyer, Charles J., Rothman, Adam J.
Format: Article
Language:English
Subjects:
Cluster analysis
Decision making models
Discriminant analysis
Estimating techniques
Joint inverse covariance matrix estimation
Matrix
Model-based clustering
Parameter estimation
Penalized Modeling for Regression and Classification
Semi-supervised learning
Studies
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Summary:This article proposes a penalized likelihood method to jointly estimate multiple precision matrices for use in quadratic discriminant analysis (QDA) and model-based clustering. We use a ridge penalty and a ridge fusion penalty to introduce shrinkage and promote similarity between precision matrix estimates. We use blockwise coordinate descent for optimization, and validation likelihood is used for tuning parameter selection. Our method is applied in QDA and semi-supervised model-based clustering.
ISSN:1061-8600
1537-2715
DOI:10.1080/10618600.2014.920709