Sparse Least Squares Twin Support Vector Machines with Manifold-preserving Graph Reduction

Xijiong Xie

Abstract

Least squares twin support vector machines are a new non-parallel hyperplane classifier, in which the primal optimization problems of twin support vector machines are modified in least square sense and inequality constraints are replaced by equality constraints. In classification problems, enhancing the robustness of least squares twin support vector machines and reducing the time complexity of kernel function evaluation of a new example when inferring the label of a new example are very important. In this paper, we propose a new sparse least squares twin support vector machines based on manifold-preserving graph reduction which is an efficient graph reduction algorithm with manifold assumption. This method first selects informative examples for positive examples and negative examples, respectively and then applies them for classification. Experimental results confirm the feasibility and effectiveness of our proposed method.

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Paper Citation


in Harvard Style

Xie X. (2018). Sparse Least Squares Twin Support Vector Machines with Manifold-preserving Graph Reduction.In Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-276-9, pages 563-567. DOI: 10.5220/0006690805630567


in Bibtex Style

@conference{icpram18,
author={Xijiong Xie},
title={Sparse Least Squares Twin Support Vector Machines with Manifold-preserving Graph Reduction},
booktitle={Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2018},
pages={563-567},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006690805630567},
isbn={978-989-758-276-9},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Sparse Least Squares Twin Support Vector Machines with Manifold-preserving Graph Reduction
SN - 978-989-758-276-9
AU - Xie X.
PY - 2018
SP - 563
EP - 567
DO - 10.5220/0006690805630567