The Optimal Design of Weighted Order Statistics Filters by Using Support Vector Machines
Chih-Chia Yao, Pao-Ta Yu · EURASIP Journal on Advances in Signal Processing · 2006
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.