Efficient cross-validation for kernelized least-squares regression with sparse basis expansions
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]
|Collections||ANU Research Publications|
|Source:||Journal of Machine Learning Research|
|Access Rights:||Open Access|
|01_Pahikkala_Efficient_cross-validation_for_2012.pdf||1.46 MB||Adobe PDF|
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