In this paper, we use the set-membership normalized least-mean-square (SM-NLMS) algorithm to censor the data set in big data applications. First, we use the distribution of the noise signal and the excess of the steady-state mean-square error (EMSE) to estimate the threshold for the desired update rate in the single threshold SM-NLMS (ST-SM-NLMS) algorithm. Then, we introduce the double threshold SM-NLMS (DT-SM-NLMS) algorithm which defines an acceptable
range of the error signal. This algorithm censors the data with very low and very high output estimation error.
Numerical results confirm the effectiveness of the estimated threshold and corroborate the superior performance of the DT-SM-NLMS algorithm.