Monthly
288 pp. per issue
6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

January 2007, Vol. 19, No. 1, Pages 283-301
(doi: 10.1162/neco.2007.19.1.283)
© 2006 Massachusetts Institute of Technology
Fast Generalized Cross-Validation Algorithm for Sparse Model Learning
Article PDF (132 KB)
Abstract

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.