Statistical inference subject to nonnegativity constraints is a frequently occurring problem in learning problems. The nonnegative least-mean-square (NNLMS) algorithm was derived to address such problems in an online way. This algorithm builds on a fixed-point iteration strategy driven by the Karush-Kuhn-Tucker conditions. It was shown to provide low variance estimates, but it however suffers from unbalanced convergence rates of these estimates. In this paper, we address this problem by introducing a variant of the NNLMS algorithm. We provide a theoretical analysis of its behavior in terms of transient learning curve, steady-state and tracking performance. We also introduce an extension of the algorithm for online sparse system identification. Monte-Carlo simulations are conducted to illustrate the performance of the algorithm and to validate the theoretical results. HighlightsWe proposed a variant of NN-LMS algorithm with balanced weight convergence rates.Accurate performance analysis is performed for a general nonstationarity model.The sparse system identification problem can be solved via the derived algorithm.
['Jie Chen', 'Cedric Richard', 'José Carlos M. Bermudez']
Reweighted nonnegative least-mean-square algorithm