A combined SVM and LDA approach for classification

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46 Scopus citations

Abstract

This paper describes a new large margin classifier, named SVM/LDA. This classifier can be viewed as an extension of support vector machine (SVM) by incorporating some global information about the data. The SVM/LDA classifier can be also seen as a generalization of linear discriminant analysis (LDA) by incorporating the idea of (local) margin maximization into standard LDA formulation. We show that existing SVM software can be used to solve the SVM/LDA formulation. We also present empirical comparisons of the proposed algorithm with SVM and LDA using both synthetic and real world benchmark data.

Original languageEnglish (US)
Title of host publicationProceedings of the International Joint Conference on Neural Networks, IJCNN 2005
Pages1455-1459
Number of pages5
Volume3
DOIs
StatePublished - Dec 1 2005
EventInternational Joint Conference on Neural Networks, IJCNN 2005 - Montreal, QC, Canada
Duration: Jul 31 2005Aug 4 2005

Other

OtherInternational Joint Conference on Neural Networks, IJCNN 2005
CountryCanada
CityMontreal, QC
Period7/31/058/4/05

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    Xiong, T., & Cherkassky, V. S. (2005). A combined SVM and LDA approach for classification. In Proceedings of the International Joint Conference on Neural Networks, IJCNN 2005 (Vol. 3, pp. 1455-1459). [1556089] https://doi.org/10.1109/IJCNN.2005.1556089