鲁棒回归之Least Median of Squares(LMedS) 简介 这里我们来看一个比较少见的鲁棒回归中的方法:最小中值二乘法(Least Median of Squares, LMedS),其对应的优化问题为: 为残差,为优化变量(1)argminβ S(β)=median(ri2),ri为残差,β为优化变量 它和最小二乘法不同,最小二乘法本质上
An approximation algorithm for least median of squares regression. Inf. Process. Lett., 63(5):237-241, 1997.C.F. Olson. An approximation algorithm for least median of squares regression. Informa- tion Processing Letters, 63:237-241, 1997....
However, it had been observed that the use of the magnitude of the spatial gradients may not be 13 ideal for certain types of image sequences where edges are prominent. If points that correspond to these edges have been used in the sub-samples, the accuracy of the algorithm is reduced. ...
to some extent robust to outliers, have been proposed. In this paper we present a new robust learning algorithm based on the iterative Least Median of Squares, that outperforms some existing
6) estimation of least median squares 最小中值平方估计 1. Then the robust Mahalanobis distances of training samples with normal texture were calculated by using the fast algorithm for the estimation of least median squares. 利用最小中值平方估计的快速算法,获得正常织物纹理训练样本的稳健马氏距离,并...
A two-dimensional adaptive algorithm which resists impulsive interference is presented. The proposed two-dimensional median least mean squares (TDMLMS) algorithm is a gradient-based steepest descent algorithm and employs the sample median of the instantaneous gradients within a suitable window as a ...
An efficient algorithm for updating the gradient adaptive lattice (GAL) filter, termed the median least mean squares lattice (MLMSL) adaptive filter, is presented. The update in the proposed algorithm is achieved by employing the sample median of the gradient estimates at each stage of the ...
An approximation algorithm for least median of squares regression. Information Processing Letters, 63(5):237{241, September 1997.C. F. Olson. An approximation algorithm for least median of squares regression. Information Processing Letters, 63(5):237-241, 1997....
We introduce a new, robust algorithm which recovers the fit corresponding to the absolute majority (at least 51 percent) of the pixels in the processing window. The algorithm uses the least median of squares (LMedS) estimator recently introduced in the statistics literature. We show that the ...
In this algorithm, the optic flow problem is first formulated as a standard least squares problem. Then, its associated closest point problem is introduced and the transformation which takes this problem to a standard regression problem is provided. The least median of squares technique is used to...