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Neural Processing Letters
Erschienen in: Neural Processing Letters 2/2019

09.05.2018

Feature Extraction Based on Support Vector Data Description

verfasst von: Li Zhang, Xingning Lu

Erschienen in: Neural Processing Letters | Ausgabe 2/2019

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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.
Vorheriger Artikel Global Convergence on Asymptotically Almost Periodic SICNNs with Nonlinear Decay Functions
Nächster Artikel Supervised Training of Spiking Neural Network by Adapting the E-MWO Algorithm for Pattern Classification

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Metadaten
Titel
Feature Extraction Based on Support Vector Data Description
verfasst von
Li Zhang
Xingning Lu
Publikationsdatum
09.05.2018
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 2/2019
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-018-9838-0

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