Data compression of nonlinear time series using a hybrid linear/nonlinear predictor
Signal Processing - Signal processing in UWB communications
Frequency-response-shaped LMS adaptive filter
Digital Signal Processing
Convergence analysis of a frequency domain adaptive filter with constraints on the output weights
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
An online AUC formulation for binary classification
Pattern Recognition
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Despite the widespread usage of the leaky LMS algorithm, there has been no detailed study of its performance. This paper presents an analytical treatment of the mean-square error (MSE) performance for the leaky LMS adaptive algorithm for Gaussian input data. The common independence assumption regarding W(n) and X(n) is also used. Exact expressions that completely characterize the second moment of the coefficient vector and algorithm steady-state excess MSE are developed. Rigorous conditions for MSE convergence are also established. Analytical results are compared with simulation and are shown to agree well