Data-selective LMS-Newton and LMS-Quasi-Newton Algorithms

The huge volume of data that are available today requires data-selective processing approaches that avoid the costs in computational complexity via appropriately treating the non-innovative data. In this paper, extensions of the well-known adaptive filtering LMS-Newton and LMS-Quasi-Newton Algorithms are developed that enable data selection while also addressing the censorship of outliers that emerge due to high measurement errors. The proposed solutions allow the prescription of how often the acquired data are expected to be incorporated into the learning process based on some a priori information regarding the environment. Simulation results on both synthetic and real-world data verify the effectiveness of the proposed algorithms that may achieve significant reductions in computational costs without sacrificing estimation accuracy due to the selection of the data.