Lazy Lasso for local regression

Vidaurre Henche, Diego and Bielza, Concha and Larrañaga Múgica, Pedro (2011). Lazy Lasso for local regression. "Computational Statistics" ; pp. 1-20. ISSN 0943-4062. https://doi.org/10.1007/s00180-011-0274-0.

Description

Title: Lazy Lasso for local regression
Author/s:
  • Vidaurre Henche, Diego
  • Bielza, Concha
  • Larrañaga Múgica, Pedro
Item Type: Article
Título de Revista/Publicación: Computational Statistics
Date: 2011
ISSN: 0943-4062
Subjects:
Freetext Keywords: Lasso – l1-regularization – Variable selection – Loess – Locally weighted regression – Sparse models – Lazy lasso – Nonparametric variable selection
Faculty: Facultad de Informática (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Locally weighted regression is a technique that predicts the response for new data items from their neighbors in the training data set, where closer data items are assigned higher weights in the prediction. However, the original method may suffer from overfitting and fail to select the relevant variables. In this paper we propose combining a regularization approach with locally weighted regression to achieve sparse models. Specifically, the lasso is a shrinkage and selection method for linear regression. We present an algorithm that embeds lasso in an iterative procedure that alternatively computes weights and performs lasso-wise regression. The algorithm is tested on three synthetic scenarios and two real data sets. Results show that the proposed method outperforms linear and local models for several kinds of scenarios

More information

Item ID: 11002
DC Identifier: http://oa.upm.es/11002/
OAI Identifier: oai:oa.upm.es:11002
DOI: 10.1007/s00180-011-0274-0
Official URL: http://www.springerlink.com/content/qr202q5851334085/
Deposited by: Memoria Investigacion
Deposited on: 05 Jun 2012 08:34
Last Modified: 20 Apr 2016 19:11
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