Open AccessData Censoring with Set-Membership Affine Projection Algorithm
Author(s) -
Gholamreza Karamali,
Akram Zardadi,
Hamid Reza Moradi
Publication year - 2020
Publication title -
computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.145
H-Index - 5
eISSN - 2300-7036
pISSN - 1508-2806
DOI - 10.7494/csci.2020.21.1.3388
Subject(s) - outlier , computer science , algorithm , affine transformation , mean squared error , data set , rate of convergence , convergence (economics) , noise (video) , range (aeronautics) , set (abstract data type) , signal (programming language) , robustness (evolution) , artificial intelligence , mathematics , statistics , computer network , channel (broadcasting) , materials science , biochemistry , chemistry , composite material , pure mathematics , economics , image (mathematics) , gene , programming language , economic growth
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 the 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 the 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.