Open Journal of Statistics
Volume 5, Issue 6 (October 2015)
ISSN Print: 2161-718X ISSN Online: 2161-7198
Google-based Impact Factor: 0.53 Citations
Random Subspace Learning Approach to High-Dimensional Outliers Detection ()
Affiliation(s)
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.
KEYWORDS
Share and Cite:
Cited by
Copyright © 2024 by authors and Scientific Research Publishing Inc.
This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.