Indefinite Core Vector Machine

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Indefinite Core Vector Machine. / Schleif, Frank-Michael; Tino, Peter.

In: Pattern Recognition, Vol. 71, 01.11.2017, p. 187-195.

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@article{6bae76b14e5140549d11a3eb35c35f49,
title = "Indefinite Core Vector Machine",
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.",
keywords = "Indefinite learning , Krĕin space , Classification , Core Vector Machine , Nystr{\"o}m , Sparse , Linear complexity",
author = "Frank-Michael Schleif and Peter Tino",
year = "2017",
month = nov
day = "1",
doi = "10.1016/j.patcog.2017.06.003",
language = "English",
volume = "71",
pages = "187--195",
journal = "Pattern Recognition",
issn = "0031-3203",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Indefinite Core Vector Machine

AU - Schleif, Frank-Michael

AU - Tino, Peter

PY - 2017/11/1

Y1 - 2017/11/1

N2 - 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.

AB - 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.

KW - Indefinite learning

KW - Krĕin space

KW - Classification

KW - Core Vector Machine

KW - Nyström

KW - Sparse

KW - Linear complexity

U2 - 10.1016/j.patcog.2017.06.003

DO - 10.1016/j.patcog.2017.06.003

M3 - Article

VL - 71

SP - 187

EP - 195

JO - Pattern Recognition

JF - Pattern Recognition

SN - 0031-3203

ER -