Robust Algorithms for Linear and Nonlinear Regression via Sparse Modeling Methods: Theory, Algorithms and Applications to Image Denoising (2016)
Abstract / truncated to 115 words
The task of robust regression is of particular importance in signal processing, statistics and machine learning. Ordinary estimators, such as the Least Squares (LS) one, fail to achieve sufficiently good performance in the presence of outliers. Although the problem has been addressed many decades ago and several methods have been established, it has recently attracted more attention in the context of sparse modeling and sparse optimization techniques. The latter is the line that has been followed in the current dissertation. The reported research, led to the development of a novel approach in the context of greedy algorithms. The model adopts the decomposition of the noise into two parts: a) the inlier noise and b) the ... toggle 5 keywordsrobust regression – greedy algorithm for robust denoising gard – robust nonlinear regression in rkhs – kernel greedy algorithm for robust denoising kgard – image denoising
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