Description
Title: SET-MEMBERSHIP AFFINE PROJECTION ALGORITHM FOR DATA CENSORING
Abstract: In this study, we censor useless and irrelevant data in big data problems using the single-threshold and double-threshold set-membership affine projection algorithms. For this reason, we use the excess of the mean squared error (EMSE) in steady-state and the probability distribution function of the additive noise in the desired signal to evaluate the threshold parameter of the single-threshold set-membership affine projection (ST-SM-AP) algorithm with the aim of obtaining the desired update percentage. To find very large errors brought on by unrelated data, we also suggest the double-threshold set-membership affine projection (DT-SM-AP) algorithm (such as outliers). The DT-SM-AP algorithm will accelerate misalignment and convergence of the learning process with low computational complexity, and it is capable of censoring non-informative and unrelated data in big data problems. The synthetic examples and actual experiments demonstrate that the proposed algorithms outperform conventional algorithms.
Keywords: adaptive filtering, machine learning, data censoring, big data
Paper Quality: SCOPUS / Web of Science Level Research Paper
Paper type: Analysis Based Research Paper
Subject: Computer Science
Writer Experience: 20+ Years
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