Communications in Statistics - Simulation and Computation· 2026Q2
Spatial sign based direct sparse linear discriminant analysis for high dimensional data
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- Q2SCImago
- 2026year
Short summary
A new method, SSLDA, uses spatial signs to achieve robust linear discriminant analysis for high-dimensional data, outperforming existing methods against heavy-tailed distributions.
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Key points
- SSLDA is a new robust linear discriminant analysis method for high-dimensional data.
- The method is designed to withstand heavy-tailed elliptical distributions.
- SSLDA achieves optimal convergence rates for misclassification and estimation errors.
- Numerical experiments confirm superior finite sample performance and robustness over existing methods.
AI-generated from the title and abstract; the full text is not read.
Abstract
This paper investigates the robust linear discriminant analysis (LDA) problem with elliptical distributions in high-dimensional data. We propose a robust classification method, named SSLDA, that is intended to withstand heavy-tailed distributions. We demonstrate that SSLDA achieves an optimal convergence rate in terms of both misclassification rate and estimate error. Our theoretical results are further confirmed by extensive numerical experiments on both simulated and real datasets. Compared with current approaches, the SSLDA method offers superior improved finite sample performance and notable robustness against heavy-tailed distributions.
The authors' abstract, as published at the source. Communications in Statistics - Simulation and Computation, 2026 · DOI ↗
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Field: Statistics and Probability
Statistics and ProbabilityMathematics