BACON: blocked adaptive computationally efficient outlier nominators
Computational Statistics & Data Analysis
Multivariate outlier detection in exploration geochemistry
Computers & Geosciences
Building Shape Models from Lousy Data
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Detecting influential observations in principal components and common principal components
Computational Statistics & Data Analysis
Robust concentration graph model selection
Computational Statistics & Data Analysis
Error rates for multivariate outlier detection
Computational Statistics & Data Analysis
Outliers detection in environmental monitoring databases
Engineering Applications of Artificial Intelligence
Detection of multivariate outliers in business survey data with incomplete information
Advances in Data Analysis and Classification
A Stahel-Donoho estimator based on huberized outlyingness
Computational Statistics & Data Analysis
A survey on unsupervised outlier detection in high-dimensional numerical data
Statistical Analysis and Data Mining
Brushing moments in interactive visual analysis
EuroVis'10 Proceedings of the 12th Eurographics / IEEE - VGTC conference on Visualization
Robust distances for outlier-free goodness-of-fit testing
Computational Statistics & Data Analysis
Flexible and adaptive subspace search for outlier analysis
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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A computationally fast procedure for identifying outliers is presented that is particularly effective in high dimensions. This algorithm utilizes simple properties of principal components to identify outliers in the transformed space, leading to significant computational advantages for high-dimensional data. This approach requires considerably less computational time than existing methods for outlier detection, and is suitable for use on very large data sets. It is also capable of analyzing the data situation commonly found in certain biological applications in which the number of dimensions is several orders of magnitude larger than the number of observations. The performance of this method is illustrated on real and simulated data with dimension ranging in the thousands.