Estimating sparse models from multivariate discrete data via transformed Lasso

被引：0

作者：

Roos, Teemu ^{[1
]}

Yu, Bin ^{[2
]}

机构：

[1] Univ Helsinki, HIIT, FIN-00014 Helsinki, Finland

[2] Univ Calif Berkeley, Dept Stat, Berkeley, CA 94720 USA

来源：

2009 INFORMATION THEORY AND APPLICATIONS WORKSHOP | 2009年

基金：

芬兰科学院;

关键词：

REGRESSION; SELECTION;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

The type of l(1) norm regularization used in Lasso and related methods typically yields sparse parameter estimates where most of the estimates are equal to zero. We study a class of estimators obtained by applying a linear transformation on the parameter vector before evaluating the l(1) norm. The resulting "transformed Lasso" yields estimates that are "smooth" in a way that depends on the applied transformation. The optimization problem is convex and can be solved efficiently using existing tools. We present two examples: the Haar transform which corresponds to variable length Markov chain (context-tree) models, and the Walsh-Hadamard transform which corresponds to linear combinations of XOR (parity) functions of binary input features.

引用

收藏

页码：287 / +

页数：2

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