Abstract
The recently proposed Krĕin space Support Vector Machine (KSVM) is an efficient classifier for indefinite learning problems, but with a non-sparse decision function. This very dense decision function prevents practical applications due to a costly out of sample extension. In this paper we provide a post processing technique to sparsify the obtained decision function of a Krĕin space SVM and variants thereof. We evaluate the influence of different levels of sparsity and employ a Nystrom approach to address large scale problems. Experiments show that our algorithm is similar efficient as the non-sparse Krĕin space Support Vector Machine but with substantially lower costs, such that also large scale problems can be processed.
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