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Indefinite Core Vector Machine
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DOI：10.1016/j.patcog.2017.06.003.png)
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Abstract
En 中文
The recently proposed Krein 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 Krein 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 trouble-some kernels having large negative eigenvalues or large numbers of negative eigenvalues. Experiments show that our algorithm is similar efficient as the Krein space Support Vector Machine but with substantially lower costs, such that also large scale problems can be processed. (C) 2017 Elsevier Ltd. All rights reserved.
Keywords:
Indefinite learning
Krein space
Classification
Core Vector Machine
Nystrom
Sparse
Linear complexity
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