Preprint Article Version 1 This version is not peer-reviewed

Data Censoring with Set-Membership Affine Projection Algorithm

Version 1 : Received: 11 October 2018 / Approved: 12 October 2018 / Online: 12 October 2018 (04:57:08 CEST)

How to cite: Moradi, H.R.; Zardadi, A. Data Censoring with Set-Membership Affine Projection Algorithm. Preprints 2018, 2018100253 (doi: 10.20944/preprints201810.0253.v1). Moradi, H.R.; Zardadi, A. Data Censoring with Set-Membership Affine Projection Algorithm. Preprints 2018, 2018100253 (doi: 10.20944/preprints201810.0253.v1).

Abstract

In this paper, the set-membership affine projection (SM-AP) algorithm is utilized to censor non-informative data in big data applications. To this end, the probability distribution of the additive noise signal and the excess of mean-squared error (EMSE) in steady-state are employed in order to estimate the threshold parameter of the single threshold SM-AP (ST-SM-AP) algorithm aiming at attaining the desired update rate. Furthermore, by defining an acceptable range for the error signal, the double threshold SM-AP (DT-SM-AP) algorithm is proposed to detect very large errors due to the irrelevant data such as outliers. The DT-SM-AP algorithm can censor non-informative and irrelevant data in big data applications, and it can improve misalignment and convergence rate of the learning process with high computational efficiency. The simulation and numerical results corroborate the superiority of the proposed algorithms over traditional algorithms.

Subject Areas

adaptive filtering; set-membership filtering; affine projection; data censoring; big data; outliers

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