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Efficient cross-validation for kernelized least-squares regression with sparse basis expansions

Pahikkala, Tapio; Suominen, Hanna; Boberg, Jorma


We propose an efficient algorithm for calculating hold-out and cross-validation (CV) type of estimates for sparse regularized least-squares predictors. Holding out H data points with our method requires O(min(H2 n,Hn2)) time provided that a predictor with n basis vectors is already trained. In addition to holding out training examples, also some of the basis vectors used to train the sparse regularized least-squares predictor with the whole training set can be removed from the basis vector set...[Show more]

CollectionsANU Research Publications
Date published: 2012
Type: Journal article
Source: Journal of Machine Learning Research
DOI: 10.1007/s10994-012-5287-6
Access Rights: Open Access


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