Multivariate Outlier Detection and Robust Covariance Matrix Estimation

Multivariate Outlier Detection and Robust Covariance Matrix Estimation

In this article, we present a simple multivariate outlier-detection procedure and a robust estimator for the covariance matrix, based on the use of information obtained from projections onto the directions that maximize and minimize the kurtosis coefŽ cient of the projected data. The properties of this estimator (computational cost, bias) are analyzed and compared with those of other robust est...

Nonsingular Robust Covariance Estimation in Multivariate Outlier Detection

Rousseeuw’s minimum covariance determinant (MCD) method is a highly robust estimator of multivariate mean and covariance. In practice, the MCD covariance estimator may be singular. However, a nonsingular covariance estimator is required to calculate the Mahalanobis distance. In order to fix this singular problem, we propose an improved version of the MCD estimator, which is a combination of the...

Robust Fundamental Matrix Estimation with Accurate Outlier Detection

The estimation of fundamental matrix from two-view images has been an important topic of research in 3D computer vision. In this paper, we present an improved robust algorithm for fundamental matrix estimation via modification of the RANSAC algorithm. The proposed algorithm is based on constructing a voting array for all the point correspondence pairs to record the consistency votes for each po...

Robust estimation of multivariate covariance components.

In many settings, such as interlaboratory testing, small area estimation in sample surveys, and heritability studies, investigators are interested in estimating covariance components for multivariate measurements. However, the presence of outliers can seriously distort estimates obtained using standard procedures such as maximum likelihood. We propose a procedure based on M-estimation for robus...

Outlier Detection via Feature Selection Algorithms in Covariance Estimation