Sundararajan, S and Shevade, Shirish and Keerthi, Sathiya S (2007) Fast Generalized Cross-Validation Algorithm for Sparse Model Learning. In: Neural Computation, 19 (1). pp. 283-301.
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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.
|Item Type:||Editorials/Short Communications|
|Additional Information:||Copyright of this article belongs to Massachusetts Institute of Technology.|
|Department/Centre:||Division of Electrical Sciences > Computer Science & Automation (Formerly, School of Automation)|
|Date Deposited:||11 Aug 2008|
|Last Modified:||19 Sep 2010 04:49|
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