PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Input selection and shrinkage in multiresponse linear regression
Timo Similä and Jarkko Tikka
Computational Statistics & Data Analysis Volume 52, Number 1, pp. 406-422, 2007. ISSN 0167-9473

Abstract

The regression problem of modeling several response variables using the same set of input variables is considered. The model is linearly parameterized and the parameters are estimated by minimizing the error sum of squares subject to a sparsity constraint. The constraint has the effect of eliminating useless inputs and constraining the parameters of the remaining inputs in the model. Two algorithms for solving the resulting convex cone programming problem are proposed. The first algorithm gives a pointwise solution, while the second one computes the entire path of solutions as a function of the constraint parameter. Based on experiments with real data sets, the proposed method has a similar performance to existing methods. In simulation experiments, the proposed method is competitive both in terms of prediction accuracy and correctness of input selection. The advantages become more apparent when many correlated inputs are available for model construction.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:3051
Deposited By:Timo Similä
Deposited On:09 October 2007