Search

Your search keyword '"ROBUST control"' showing total 4 results
4 results on '"ROBUST control"'

1. Robust normal estimation for point clouds with sharp features

Author

Li, Bao, Schnabel, Ruwen, Klein, Reinhard, Cheng, Zhiquan, Dang, Gang, and Jin, Shiyao

Subjects
*ESTIMATION theory, *SET theory, *ALGORITHMS, *ROBUST control, *RELIABILITY in engineering, *DATA analysis
Abstract

Abstract: This paper presents a novel technique for estimating normals on unorganized point clouds. Methods from robust statistics are used to detect the best local tangent plane for each point. Therefore the algorithm is capable to deal with points located in high curvature regions or near/on complex sharp features, while being highly robust with respect to noise and outliers. In particular, the presented method reliably recovers sharp features but does not require tedious manual parameter tuning as done by current methods. The key ingredients of our approach are a robust noise-scale estimator and a kernel density estimation (KDE) based objective function. In contrast to previous approaches the noise-scale estimation is not affected by sharp features and achieves high accuracy even in the presence of outliers. In addition, our normal estimation procedure allows detection and elimination of outliers. We confirm the validity and reliability of our approach on synthetic and measured data and demonstrate applications to point cloud denoising. [Copyright &y& Elsevier]

Published
2010
Full Text
View/download PDF

2. Robust tools for the imperfect world.

Author

Filzmoser, Peter and Todorov, Valentin

Subjects
*ROBUST statistics, *ROBUST control, *DATA analysis, *MULTIVARIATE analysis, *LITERATURE reviews, *ALGORITHMS, *APPLICATION software, *ESTIMATION theory
Abstract

Abstract: Data outliers or other data inhomogeneities lead to a violation of the assumptions of traditional statistical estimators and methods. Robust statistics offers tools that can reliably work with contaminated data. Here, outlier detection methods in low and high dimension, as well as important robust estimators and methods for multivariate data are reviewed, and the most important references to the corresponding literature are provided. Algorithms are discussed, and routines in R are provided, allowing for a straightforward application of the robust methods to real data. [Copyright &y& Elsevier]

Published
2013
Full Text
View/download PDF

3. Robust variable selection through MAVE.

Author

Yao, Weixin and Wang, Qin

Subjects
*ROBUST control, *MATHEMATICAL variables, *VARIANCES, *DIMENSION reduction (Statistics), *DATA analysis, *ESTIMATION theory, *ALGORITHMS
Abstract

Abstract: Dimension reduction and variable selection play important roles in high dimensional data analysis. The sparse MAVE, a model-free variable selection method, is a nice combination of shrinkage estimation, Lasso, and an effective dimension reduction method, MAVE (minimum average variance estimation). However, it is not robust to outliers in the dependent variable because of the use of least-squares criterion. A robust variable selection method based on sparse MAVE is developed, together with an efficient estimation algorithm to enhance its practical applicability. In addition, a robust cross-validation is also proposed to select the structural dimension. The effectiveness of the new approach is verified through simulation studies and a real data analysis. [Copyright &y& Elsevier]

Published
2013
Full Text
View/download PDF

4. Robust mixture modeling based on scale mixtures of skew-normal distributions

Author

Basso, Rodrigo M., Lachos, Víctor H., Cabral, Celso Rômulo Barbosa, and Ghosh, Pulak

Subjects
*ROBUST control, *MIXTURE distributions (Probability theory), *DISTRIBUTION (Probability theory), *DATA analysis, *ALGORITHMS, *MAXIMUM likelihood statistics, *ESTIMATION theory
Abstract

Abstract: A flexible class of probability distributions, convenient for modeling data with skewness behavior, discrepant observations and population heterogeneity is presented. The elements of this family are convex linear combinations of densities that are scale mixtures of skew-normal distributions. An EM-type algorithm for maximum likelihood estimation is developed and the observed information matrix is obtained. These procedures are discussed with emphasis on finite mixtures of skew-normal, skew-t, skew-slash and skew contaminated normal distributions. In order to examine the performance of the proposed methods, some simulation studies are presented to show the advantage of this flexible class in clustering heterogeneous data and that the maximum likelihood estimates based on the EM-type algorithm do provide good asymptotic properties. A real data set is analyzed, illustrating the usefulness of the proposed methodology. [ABSTRACT FROM AUTHOR]

Published
2010
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results