Becquart, ColombeIdRefORCIDORCID: https://orcid.org/0009-0003-0790-3720, Archimbaud, AuroreIdRefORCIDORCID: https://orcid.org/0000-0002-6511-9091, Ruiz-Gazen, AnneIdRefORCIDORCID: https://orcid.org/0000-0001-8970-8061, Prlić, LukaIdRef and Nordhausen, KlausIdRefORCIDORCID: https://orcid.org/0000-0002-3758-8501 (2026) Invariant Coordinate Selection and Fisher discriminant subspace beyond the case of two group. Journal of Multivariate Analysis, Vol. 211 (n°105521).

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Identification Number : 10.1016/j.jmva.2025.105521

Abstract

Invariant Coordinate Selection (ICS) is a multivariate technique that relies on the simultaneous diagonalization of two scatter matrices. It serves various purposes, including its use as a dimension reduction tool prior to clustering or outlier detection. ICS’s theoretical foundation establishes why and when the identified subspace should contain relevant information by demonstrating its connection with the Fisher discriminant subspace (FDS). These general results have been examined in detail primarily for specific scatter combinations within a two-cluster framework. In this study, we expand these investigations to include more clusters and scatter combinations. Our analysis reveals the importance of distinguishing whether the group centers matrix has full rank. In the full-rank case, we establish deeper connections between ICS and FDS. We provide a detailed study of these relationships for three clusters when the group centers matrix has full rank and when it does not. Based on these expanded theoretical insights and supported by numerical studies, we conclude that ICS is indeed suitable for recovering the FDS under very general settings and cases of failure seem rare.

Item Type: Article
Language: English
Date: January 2026
Refereed: Yes
Uncontrolled Keywords: Dimension reduction, Mixture of elliptical distributions, Scatter matrix, Simultaneous diagonalization, Subspace estimation
Subjects: B- ECONOMIE ET FINANCE
Divisions: TSE-R (Toulouse)
Site: UT1
Date Deposited: 03 Feb 2026 09:02
Last Modified: 03 Feb 2026 09:15
OAI Identifier: oai:tse-fr.eu:131181
URI: https://publications.ut-capitole.fr/id/eprint/51703

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