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dc.contributor.authorPrice, Bradley S.
dc.contributor.authorSherwood, Ben
dc.identifier.citationPrice, B. S. and Sherwood, B. (2018) A Cluster Elastic Net for Multivariate Regression . Journal of Machine Learning Research, 18, 1-39.en_US
dc.description.abstractWe propose a method for simultaneously estimating regression coefficients and clustering response variables in a multivariate regression model, to increase prediction accuracy and give insights into the relationship between response variables. The estimates of the regression coefficients and clusters are found by using a penalized likelihood estimator, which includes a cluster fusion penalty, to shrink the difference in fitted values from responses in the same cluster, and an L1 penalty for simultaneous variable selection and estimation. We propose a two-step algorithm, that iterates between k-means clustering and solving the penalized likelihood function assuming the clusters are known, which has desirable parallel computational properties obtained by using the cluster fusion penalty. If the response variable clusters are known a priori then the algorithm reduces to just solving the penalized likelihood problem. Theoretical results are presented for the penalized least squares case, including asymptotic results allowing for p≫n. We extend our method to the setting where the responses are binomial variables. We propose a coordinate descent algorithm for the normal likelihood and a proximal gradient descent algorithm for the binomial likelihood, which can easily be extended to other generalized linear model (GLM) settings. Simulations and data examples from business operations and genomics are presented to show the merits of both the least squares and binomial methods.en_US
dc.publisherJournal of Machine Learning Researchen_US
dc.rightsCopyright 2018 Bradley S. Price and Ben Sherwood. License: CC-BY 4.0, see Attribution requirements are providedat
dc.subjectMultivariate Regressionen_US
dc.subjectFusion Penaltyen_US
dc.titleA Cluster Elastic Net for Multivariate Regressionen_US
kusw.kuauthorSherwood, Ben
kusw.oanotesPer SHERPA/RoMEO 12/06/2019 Author's Pre-print: green tick author can archive pre-print (ie pre-refereeing) Author's Post-print: green tick author can archive post-print (ie final draft post-refereeing) Publisher's Version/PDF: green tick author can archive publisher's version/PDF General Conditions:

On open access repositories Authors retain copyright Publisher's version/PDF may be used Publisher's version/PDF will be deposited in ACM's computing repository (CORR) automatically Must link to publisher version Published source must be acknowledged

Mandated OA: (Awaiting information) Notes:

Publisher last contacted on 17/07/2015 All titles are open access journals
kusw.oaversionScholarly/refereed, publisher versionen_US
kusw.oapolicyThis item meets KU Open Access policy criteria.en_US

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Copyright 2018 Bradley S. Price and Ben Sherwood. License:  CC-BY 4.0, see  Attribution requirements are providedat
Except where otherwise noted, this item's license is described as: Copyright 2018 Bradley S. Price and Ben Sherwood. License: CC-BY 4.0, see Attribution requirements are providedat