Indefinite Core Vector Machine

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Colleges, School and Institutes

Abstract

The recently proposed Krĕin space Support Vector Machine (KSVM) is an efficient classifier for indefinite learning problems, but with quadratic to cubic complexity and a non-sparse decision function. In this paper a Krĕin space Core Vector Machine (iCVM) solver is derived. A sparse model with linear runtime complexity can be obtained under a low rank assumption. The obtained iCVM models can be applied to indefinite kernels without additional preprocessing. Using iCVM one can solve CVM with usually troublesome kernels having large negative eigenvalues or large numbers of negative eigenvalues. Experiments show that our algorithm is similar efficient as the Krĕin space Support Vector Machine but with substantially lower costs, such that also large scale problems can be processed.

Details

Original languageEnglish
Pages (from-to)187-195
JournalPattern Recognition
Volume71
Early online date3 Jun 2017
Publication statusPublished - 1 Nov 2017

Keywords

  • Indefinite learning , Krĕin space , Classification , Core Vector Machine , Nyström , Sparse , Linear complexity