On a Stahel-Donoho estimator with skewness-based random projection directions.
Santiago Ortiz & Omar Becerra
What the paper says
This work introduces a novel version of the Stahel-Donoho multivariate outlier detection procedure, which considers 5p + 1 specific random directions, where p is the dimensionality of the data, that is, the number of variables in the dataset.These directions are derived by maximizing the squared third sample moment of the projected observations, which then serves as a seed to obtain 5p additional directions via a stratified sampling.Compared with the standard Stahel-Donoho estimator and other outlier detection methods, this new version exhibits competitive performance across various high-dimensional datasets and contamination scenarios.By leveraging maximum skewness projection within the Stahel-Donoho framework, the proposed estimator maintains stable results in high dimensions, showing its advantage in efficiently handling complex data structures.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.00 × 0.4 = 0.00 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.