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(Neural Computation. 2006;19:283-301.)
© 2006 The MIT Press

Letter

Fast Generalized Cross-Validation Algorithm for Sparse Model Learning

S. Sundararajan

zensid{at}yahoo.com Philips Electronics India Ltd., Ulsoor, Bangalore, India

Shirish Shevade

shirish{at}csa.iisc.ernet.in Computer Science and Automation, Indian Institute of Science, Bangalore, India

S. Sathiya Keerthi

selvarak{at}yahoo-inc.com Yahoo! Research, 2821 Mission College Blvd., Santa Clara, CA 95054, USA

We propose a fast, incremental algorithm for designing linear regression models. The proposed algorithm generates a sparse model by optimizing multiple smoothing parameters using the generalized cross-validation approach. The performances on synthetic and real-world data sets are compared with other incremental algorithms such as Tipping and Faul's fast relevance vector machine, Chen et al.'s orthogonal least squares, and Orr's regularized forward selection. The results demonstrate that the proposed algorithm is competitive.






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Copyright © 2006 by The MIT Press.