Work

Unsupervised linear discrimination using skewness

Year: 2025

Type: article

Abstract: It is well-known that, in Gaussian two-group separation, the optimally discriminating projection direction can be estimated without any knowledge on the group labels. In this work, we gather several such unsupervised estimators based on skewness and derive their limiting distributions. As one of our main results, we show that all affine equivariant estimators of the optimal direction have proportional asymptotic covariance matrices, making their comparison straightforward. Two of our four estimators are novel and two have been proposed already earlier. We use simulations to verify our results and to inspect the finite-sample behaviors of the estimators.

Source: Journal of Multivariate Analysis

DOI: 10.1016/j.jmva.2025.105524

Authors: Una Radojičić, Klaus Nordhausen, Joni Virta

Institutions: TU Wien, University of Helsinki, University of Jyväskylä, University of Turku

Language: English

Topic: Statistical Methods and Applications

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,310

Funders: European Cooperation in Science and Technology, Academy of Finland, Austrian Science Fund