Robust L -Norm Distance Enhanced Multi-Weight Vector Projection Support Vector Machine

The enhanced multi-weight vector projection support vector machine (EMVSVM) is an outstanding algorithm for binary classification, which is proposed recently. However, it measures the distances in an objective function by the squared L_{2} -norm, which exaggerates the effects of outliers or noisy d...

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Bibliographic Details
Published in:IEEE access 2019, Vol.7, p.3275-3286
Main Authors: Zhao, Henghao, Ye, Qiaolin, Naiem, Meen Abdullah, Fu, Liyong
Format: Article
Language:English
Subjects:
EMVSVM
L₂,₁-norm
L₂-norm
norm
robustness
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Summary:The enhanced multi-weight vector projection support vector machine (EMVSVM) is an outstanding algorithm for binary classification, which is proposed recently. However, it measures the distances in an objective function by the squared L_{2} -norm, which exaggerates the effects of outliers or noisy data. In order to alleviate this problem, we propose an effective novel EMVSVM, termed robust EMVSVM based on the L 2,1 -norm distance (L 2,1 -EMVSVM). The distances in the objective of our algorithm are measured by the L 2,1 -norm. Besides, a new powerful iterative algorithm is designed to solve the formulated objective, whose convergence is ensured by theoretical proofs. Finally, the effectiveness and robustness of L 2,1 -EMVSVM are verified through extensive experiments.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2018.2879052