Showing papers on "RANSAC published in 1990"
TL;DR: This work proposes to compute distances based on very robust estimates of location and covariance, better suited to expose the outliers in a multivariate point cloud, to avoid the masking effect.
Abstract: Detecting outliers in a multivariate point cloud is not trivial, especially when there are several outliers. The classical identification method does not always find them, because it is based on the sample mean and covariance matrix, which are themselves affected by the outliers. That is how the outliers get masked. To avoid the masking effect, we propose to compute distances based on very robust estimates of location and covariance. These robust distances are better suited to expose the outliers. In the case of regression data, the classical least squares approach masks outliers in a similar way. Also here, the outliers may be unmasked by using a highly robust regression method. Finally, a new display is proposed in which the robust regression residuals are plotted versus the robust distances. This plot classifies the data into regular observations, vertical outliers, good leverage points, and bad leverage points. Several examples are discussed.
1,419 citations
TL;DR: In this article, the authors propose to compute distances based on very robust estimates of location and covariance, which may be unmasked by using a highly robust regression method, and a new display is proposed in which the robust regression residuals are plotted versus the robust distances.
Abstract: Detecting outliers in a multivariate point cloud is not trivial, especially when there are several outliers. The classical identification method does not always find them, because it is based on the sample mean and covariance matrix, which are themselves affected by the outliers. To avoid this masking effect, we propose to compute distances based on very robust estimates of location and covariance. In the case of regression data, the outliers also may be unmasked by using a highly robust regression method. A new display is proposed in which the robust regression residuals are plotted versus the robust distances
156 citations
TL;DR: In this paper, an iterative identification method for a linear state-space model with outliers and missing data is proposed by applying the Expectation-Maximization (EM) algorithm.
14 citations