Efficient cross-validation for kernelized least-squares regression with sparse basis expansions
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Pahikkala, Tapio; Suominen, Hanna; Boberg, Jorma
Description
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 |
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Date published: | 2012 |
Type: | Journal article |
URI: | http://hdl.handle.net/1885/67949 |
Source: | Journal of Machine Learning Research |
DOI: | 10.1007/s10994-012-5287-6 |
Access Rights: | Open Access |
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File | Description | Size | Format | Image |
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01_Pahikkala_Efficient_cross-validation_for_2012.pdf | 1.46 MB | Adobe PDF |
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