Summary In this paper, we propose the improved feature least‐mean‐square (IF‐LMS) algorithm to exploit hidden sparsity in unknown systems. Recently, the feature least‐mean‐square (F‐LMS) algorithm has been introduced, but its application is limited to particular systems since it uses predetermined feature matrices. However, the proposed IF‐LMS algorithm utilizes the stochastic gradient descent (SGD) method to learn feature matrices; thus, it can be used in any system that the classical LMS algorithm is applicable. Hence, by employing a learnable feature matrix, the IF‐LMS algorithm has a vast application area as compared to the F‐LMS algorithm. Moreover, mathematically, we discuss some parameters of the IF‐LMS algorithm. Simulation results, in synthetic and real‐life scenarios, demonstrate that the IF‐LMS algorithm has superior filtering accuracy to the well‐known LMS algorithm.
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