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Reweighted nonnegative least-mean-square algorithm

机译：加权非负最小均方算法

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摘要

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.
机译：受非负约束的统计推断是学习问题中经常发生的问题。得出了非负最小均方（NNLMS）算法，以在线方式解决了此类问题。该算法建立在由Karush-Kuhn-Tucker条件驱动的定点迭代策略的基础上。它显示出提供了低方差估计值，但是却遭受这些估计值收敛速度不平衡的困扰。在本文中，我们通过引入NNLMS算法的变体来解决此问题。我们从瞬态学习曲线，稳态和跟踪性能方面对其行为进行了理论分析。我们还介绍了在线稀疏系统识别算法的扩展。进行了蒙特卡洛仿真，以说明算法的性能并验证理论结果。

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