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Fritsch, V., Varoquaux, G., Thyreau, B., Poline, J.-B. and Thirion, B. (2011) Detecting Outlying Subjects in High-Dimensional Neuroimaging Datasets with Regularized Minimum Covariance Determinan 225. In: Fichtinger, G., Martel, A. and Peters, T., Eds., Medical Image Computing and Computer-Assisted Intervention MICCAI 2011, Springer, Berlin Heidelberg, 264-271.

has been cited by the following article:

• TITLE: Random Subspace Learning Approach to High-Dimensional Outliers Detection

  AUTHORS: Bohan Liu, Ernest Fokoué

  KEYWORDS: High-Dimensional, Robust, Outlier Detection, Contamination, Large p Small n, Random Subspace Method, Minimum Covariance Determinant

  JOURNAL NAME: Open Journal of Statistics, Vol.5 No.6, October 30, 2015

  ABSTRACT: We introduce and develop a novel approach to outlier detection based on adaptation of random subspace learning. Our proposed method handles both high-dimension low-sample size and traditional low-dimensional high-sample size datasets. Essentially, we avoid the computational bottleneck of techniques like Minimum Covariance Determinant (MCD) by computing the needed determinants and associated measures in much lower dimensional subspaces. Both theoretical and computational development of our approach reveal that it is computationally more efficient than the regularized methods in high-dimensional low-sample size, and often competes favorably with existing methods as far as the percentage of correct outlier detection are concerned.