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This paper studies an M-estimation-based method for linear estimation with weighted L1 regularization and its recursive implementation. Motivated by the sensitivity of conventional least-squares-based L1-regularized linear estimation (Lasso) in impulsive noise environment, an M-estimator-based Lasso (M-Lasso) method is introduced to restrain the outliers and an iterative re-weighted least-squares...
This paper studies the convergence behaviors of the noise-constrained normalized least mean squares (NCNLMS) algorithm recently proposed in the work of Chan et al. (2008). Like its LMS counterpart, the NCNLMS algorithm employs the prior knowledge of the additive noise to adjust its step-size. Following (Wei et al., 2001), the convergence behaviors of the NCLMS under the noise mismatch cases are firstly...
This paper proposes a new two-stage method for restoring image corrupted by additive impulsive and Gaussian noise based on local polynomial regression (LPR) and edge preserving regularization. In LPR, the observations are modeled locally by a polynomial using least-squares criterion with a kernel controlled by a certain bandwidth matrix. A refined intersection confidence intervals (RICI) adaptive...
This paper undertakes a comparative study of adaptive signal enhancers (ASE) of somatosensory evoked potentials (SEP) for spinal cord compression detection. We compare the ASE methods based on two adaptive filtering algorithms: the least mean squares (LMS) and Kalman filter (KF) in terms of their convergence rate, variability, and complexity. In addition, the two ASE methods are compared with the...
This paper proposes a new noise-constrained normalized least mean squares (NC-NLMS) adaptive filtering algorithm and studies its mean and mean square convergence behaviors. The new NC-NLMS algorithm is obtained by extending the noise-constrained LMS (NC-LMS) algorithm of Wei, which was proposed to explore the prior information on the noise variance in identifying unknown finite impulse response channels...
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