Subspace rotations for high-dimensional outlier detection

Hee Cheol Chung and Jeongyoun Ahn

Journal of Multivariate Analysis, 2021, vol. 183, issue C

Abstract: We propose a new two-stage procedure for detecting multiple outliers when the dimension of the data is much larger than the available sample size. In the first stage, the data are split into two disjoint sets, one containing non-outliers and the other containing the rest of the data that are considered as potential outliers. In the second stage, a series of hypothesis tests is conducted to test the abnormality of each potential outlier. A nonparametric test based on uniform random rotations is adopted for hypothesis testing. The power of the proposed test is studied under a high-dimensional asymptotic framework, and its finite-sample exactness is established under mild conditions. Numerical studies based on simulated examples and face recognition data suggest that the proposed approach is superior to the existing methods, especially in terms of false identification of non-outliers.

Keywords: Group invariance; High dimension and low sample size data; Left-spherical family; Orthogonal group; Randomization test; Stiefel manifold (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0047259X20302943
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:jmvana:v:183:y:2021:i:c:s0047259x20302943

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

DOI: 10.1016/j.jmva.2020.104713

Access Statistics for this article

Journal of Multivariate Analysis is currently edited by de Leeuw, J.

More articles in Journal of Multivariate Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().