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Feature Extraction Based on Support Vector Data Description

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Abstract

Motivated by the improvement of performance and reduction of complexity, feature extraction is referred to one manner of dimensionality reduction. This paper presents a new feature extraction method based on support vector data description (FE-SVDD). First, the proposed method establishes hyper-sphere models for each category of the given data using support vector data description. Second, FE-SVDD calculates the distances between data points and the centers of the hyper-spheres. Finally, the ratios of the distances to the radii of the hyper-spheres are treated as new extracted features. Experimental results on different data sets indicate that FE-SVDD can speed up the procedure of feature extraction and extract the distinctive information of original data.

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Acknowledgements

We would like to thank anonymous reviewers and Editor for their valuable comments and suggestions, which have significantly improved this paper. This work was supported in part by the National Natural Science Foundation of China under Grant No. 61373093, by the Soochow Scholar Project, by the Six Talent Peak Project of Jiangsu Province of China, and by the Collaborative Innovation Center of Novel Software Technology and Industrialization.

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Authors and Affiliations

  1. School of Computer Science and Technology & Joint International Research Laboratory of Machine Learning and Neuromorphic Computing, Soochow University, Suzhou, 215006, Jiangsu, China

    Li Zhang & Xingning Lu

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  1. Li Zhang
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  2. Xingning Lu
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Correspondence to Li Zhang.

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This work was supported in part by the National Natural Science Foundation of China under Grant No. 61373093, by the Natural Science Foundation of Jiangsu Province of China under Grant No. BK20140008, and by the Soochow Scholar Project.

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Zhang, L., Lu, X. Feature Extraction Based on Support Vector Data Description. Neural Process Lett 49, 643–659 (2019). https://doi.org/10.1007/s11063-018-9838-0

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