Ridge Fusion in Statistical Learning

Bradley S. Price; Charles J. Geyer; Adam J. Rothman

doi:10.1080/10618600.2014.920709

Ridge Fusion in Statistical Learning

Bradley S. Price, Charles J. Geyer, Adam J. Rothman

Statistics (Twin Cities)

Research output: Contribution to journal › Article › peer-review

19 Scopus citations

Abstract

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.

Original language	English (US)
Pages (from-to)	439-454
Number of pages	16
Journal	Journal of Computational and Graphical Statistics
Volume	24
Issue number	2
DOIs	https://doi.org/10.1080/10618600.2014.920709
State	Published - Apr 3 2015

Bibliographical note

Publisher Copyright:
© 2015, © American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America.

Keywords

Discriminant analysis
Joint inverse covariance matrix estimation
Model-based clustering
Semi-supervised learning

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abstract = "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.",

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AB - 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.

KW - Discriminant analysis

KW - Joint inverse covariance matrix estimation

KW - Model-based clustering

KW - Semi-supervised learning

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Ridge Fusion in Statistical Learning

Abstract

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Keywords

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