An Improved Noise Reduction Algorithm Based on Manifold Learning and Its Application to Signal Noise Reduction

Abstract:

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In the noise reduction algorithm based on manifold learning, phase space data may be distorted and reduction targets are chosen at random, it made efficiency and effect of noise reduction lower.To solve this problem, a improved noise reducation method (local tangent space mean reconstruction) was proposed.The process of global array by affine transformation will be replaced with mean reconstruction,and the intrinsic dimension was estimate as dimension of reduction targets by using maximum likehood estimation method, the data in addition to intrinsic dimension space will be eliminated.Noise reduction experiment to fan vibration signal with noise shows this method had better noise reduction effect.

Info:

Periodical:

Edited by:

Zhenyu Du and Bin Liu

Pages:

653-656

DOI:

10.4028/www.scientific.net/AMM.26-28.653

Citation:

G. B. Wang and L. P. Huang, "An Improved Noise Reduction Algorithm Based on Manifold Learning and Its Application to Signal Noise Reduction", Applied Mechanics and Materials, Vols. 26-28, pp. 653-656, 2010

Online since:

June 2010

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Price:

$35.00

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