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IJCNN
2006
IEEE

Common Subset Selection of Inputs in Multiresponse Regression

14 years 5 months ago
Common Subset Selection of Inputs in Multiresponse Regression
— We propose the Multiresponse Sparse Regression algorithm, an input selection method for the purpose of estimating several response variables. It is a forward selection procedure for linearly parameterized models, which updates with carefully chosen step lengths. The step length rule extends the correlation criterion of the Least Angle Regression algorithm for many responses. We present a general concept and explicit formulas for three different variants of the algorithm. Based on experiments with simulated data, the proposed method competes favorably with other methods when many correlated inputs are available for model construction. We also study the performance with several real data sets.
Timo Similä, Jarkko Tikka
Added 11 Jun 2010
Updated 11 Jun 2010
Type Conference
Year 2006
Where IJCNN
Authors Timo Similä, Jarkko Tikka
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