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The Optimal Design of Weighted Order Statistics Filters by Using Support Vector Machines

EURASIP Journal on Advances in Signal Processing volume 2006, Article number: 024185 (2006) Cite this article

Abstract

Support vector machines (SVMs), a classification algorithm for the machine learning community, have been shown to provide higher performance than traditional learning machines. In this paper, the technique of SVMs is introduced into the design of weighted order statistics (WOS) filters. WOS filters are highly effective, in processing digital signals, because they have a simple window structure. However, due to threshold decomposition and stacking property, the development of WOS filters cannot significantly improve both the design complexity and estimation error. This paper proposes a new designing technique which can improve the learning speed and reduce the complexity of designing WOS filters. This technique uses a dichotomous approach to reduce the Boolean functions from 255 levels to two levels, which are separated by an optimal hyperplane. Furthermore, the optimal hyperplane is gotten by using the technique of SVMs. Our proposed method approximates the optimal weighted order statistics filters more rapidly than the adaptive neural filters.

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, College of Engineering, National Chung Cheng University, Chia-yi, 62107, Taiwan

    Chih-Chia Yao & Pao-Ta Yu

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  1. Chih-Chia Yao
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  2. Pao-Ta Yu
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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Yao, CC., Yu, PT. The Optimal Design of Weighted Order Statistics Filters by Using Support Vector Machines. EURASIP J. Adv. Signal Process. 2006, 024185 (2006). https://doi.org/10.1155/ASP/2006/24185

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