PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Multiresponse sparse regression with application to multidimensional scaling
Timo Similä and Jarkko Tikka
In: Artificial Neural Networks: Biological Inspirations – ICANN 2005, 11-15 Sep 2005, Warsaw, Poland.

Abstract

Sparse regression is the problem of selecting a parsimonious subset of all available regressors for an efficient prediction of a target variable. We consider a general setting in which both the target and regressors may be multivariate. The regressors are selected by a forward selection procedure that extends the Least Angle Regression algorithm. Instead of the common practice of estimating each target variable individually, our proposed method chooses sequentially those regressors that allow, on average, the best predictions of all the target variables. We illustrate the procedure by an experiment with artifcial data. The method is also applied to the task of selecting relevant pixels from images in multidimensional scaling of handwritten digits.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:1717
Deposited By:Timo Similä
Deposited On:28 November 2005