Sparse group lasso and high dimensional multinomial classification

Martin Vincent; N.R. Hansen

doi:10.1016/j.csda.2013.06.004

Sparse group lasso and high dimensional multinomial classification

Martin Vincent, N.R. Hansen

Institut for Matematiske Fag

65 Citationer (Scopus)

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Abstract

The sparse group lasso optimization problem is solved using a coordinate gradient descent algorithm. The algorithm is applicable to a broad class of convex loss functions. Convergence of the algorithm is established, and the algorithm is used to investigate the performance of the multinomial sparse group lasso classifier. On three different real data examples the multinomial group lasso clearly outperforms multinomial lasso in terms of achieved classification error rate and in terms of including fewer features for the classification. An implementation of the multinomial sparse group lasso algorithm is available in the R package msgl. Its performance scales well with the problem size as illustrated by one of the examples considered - a 50 class classification problem with 10 k features, which amounts to estimating 500 k parameters.

Originalsprog	Engelsk
Tidsskrift	Computational Statistics & Data Analysis
Vol/bind	71
Sider (fra-til)	771-786
Antal sider	16
ISSN	0167-9473
DOI	https://doi.org/10.1016/j.csda.2013.06.004
Status	Udgivet - 1 mar. 2014

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10.1016/j.csda.2013.06.004

1205.1245v2Indsendt manuskript, 472 KBLicens: CC BY-ND

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Sparse group lasso and high dimensional multinomial classification

Abstract

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