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Erschienen in:
2005 | OriginalPaper | Buchkapitel
Multiresponse Sparse Regression with Application to Multidimensional Scaling
verfasst von : Timo Similä, Jarkko Tikka
Erschienen in: Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005
Verlag: Springer Berlin Heidelberg
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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 artificial data. The method is also applied to the task of selecting relevant pixels from images in multidimensional scaling of handwritten digits.