Adaptive Filter Theory 4th Edition

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Distribution-Free One-Pass Learning (2017, methodology): “getting factor recursive least square [14], we also proposed a variant for the high-dimensional scenario by exploiting the technique from compressed sensing, therefore our approach enjoys the one-pass property.”
Adaptive Reduced-Rank Processing Based on Joint and Iterative Interpolation, Decimation, and Filtering (2009, background): “In order to facilitate the description of the scheme, let us introduce an alternative way of expressing the vector , that will be useful in the following through the equivalence: (7) where the matrix with the samples of has a Hankel structure [27] and is described by . . . . . . . . . . .”
Estimation of the forgetting factor in kernel recursive least squares (2012, background): “The standard RLS formulation often includes a forgetting factor λ ∈ (0, 1] that allows it to deal with non-stationary systems to some extent, while in order to obtain true tracking capabilities a more general extended recursive least-squares (EX-RLS) algorithm is required [1, 2].”