Data censoring with set-membership affine projection algorithm

Abstract

In this work, we use the single-threshold and double-threshold set-membership affine projection algorithm to censor non-informative and irrelevant data in big data problems. For this purpose, we employ the probability distribution function of the additive noise in the desired signal and the excess of the meansquared error (EMSE) in steady-state to evaluate the threshold parameter of the single -threshold set-membership affine projection (ST-SM-AP) algorithm intending to obtain the desired update percentage. In addition, we propose the double-threshold set-membership affine projection (DT-SM-AP) algorithm to detect very large errors caused by unrelated data (such as outliers). The DT-SM-AP algorithm is capable of censoring non-informative and unrelated data in big data problems, and it will promote the misalignment and convergence speed of the learning procedure with low computational complexity. The synthetic examples and real-life experiments substantiate the superior performance of the proposed algorithms as compared to traditional algorithms.