Selecting Ridge Parameters in Infinite Dimensional Hypothesis Spaces
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
An introduction to boosting and leveraging
Advanced lectures on machine learning
The subspace information criterion for infinite dimensional hypothesis spaces
The Journal of Machine Learning Research
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Nonquadratic regularizers, in particular the l1 norm regularizer can yield sparse solutions that generalize well. In this work we propose the generalized subspace information criterion (GSIC) that allows to predict the generalization error for this useful family of regularizers. We show that under some technical assumptions GSIC is an asymptotically unbiased estimator of the generalization error. GSIC is demonstrated to have a good performance in experiments with the l1 norm regularizer as we compare with the network information criterion (NIC) and cross- validation in relatively large sample cases. However in the small sample case, GSIC tends to fail to capture the optimal model due to its large variance. Therefore, also a biased version of GSIC is introduced,which achieves reliable model selection in the relevant and challenging scenario of high-dimensional data and few samples