Eigenvalue Proximal Support Vector Machine Algorithm Based on Eigenvalue Decoposition

CHEN Sugen; WU Xiaojun

doi:10.11999/JEIT150693

Volume 38 Issue 3

Mar. 2016

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(3): 557-564

CHEN Sugen, WU Xiaojun. Eigenvalue Proximal Support Vector Machine Algorithm Based on Eigenvalue Decoposition[J]. Journal of Electronics & Information Technology, 2016, 38(3): 557-564. doi: 10.11999/JEIT150693

Citation:

CHEN Sugen, WU Xiaojun. Eigenvalue Proximal Support Vector Machine Algorithm Based on Eigenvalue Decoposition[J]. Journal of Electronics & Information Technology, 2016, 38(3): 557-564. doi: 10.11999/JEIT150693

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Eigenvalue Proximal Support Vector Machine Algorithm Based on Eigenvalue Decoposition

doi: 10.11999/JEIT150693

CHEN Sugen¹,
WU Xiaojun²

1.
2.
(School of IoT Engineering, Jiangnan University, Wuxi 214122, China)

Funds:

The National Natural Science Foundation of China (61373055, 61103128), 111 Project of Chinese Ministry of Education (B12018), Industrial Support Program of Jiangsu Province (BE2012031)

Received Date: 2015-06-08
Rev Recd Date: 2015-09-17
Publish Date: 2016-03-19

Abstract

Abstract

To deal with the consistency problem of training process and decision process in Generalized Eigenvalue Proximal Support Vector Machine (GEPSVM), an improved version of eigenvalue proximal support vector machine, called IGEPSVM for short is proposed. At first, IGEPSVM for binary classification problem is proposed, and then Multi-IGEPSVM is also presented for multi-class classification problem based on one-versus-rest strategy. The main contributions of this paper are as follows. The generalized eigenvalue decomposition problems are replaced by the standard eigenvalue decomposition problems, leading to simpler optimization problems. An extra parameter is introduced, which can adjust the performance of the model and improve the classification accuracy of GEPSVM. A corresponding multi-class classification algorithm is proposed, which is not studied in GEPSVM. Experimental results on several datasets illustrate that IGEPSVM is superior to GEPSVM in both classification accuracy and training speed.
- Support Vector Machine (SVM),
- Generalized Eigenvalue Proximal SVM (GEPSVM),
- Binary classification,
- Multi-class classification,
- Eigenvalue decoposition

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