Robust classification with flexible discriminant analysis in heterogeneous data - Archive ouverte HAL
Communication Dans Un Congrès Année : 2022

Robust classification with flexible discriminant analysis in heterogeneous data

Résumé

Linear and Quadratic Discriminant Analysis are well-known classical methods but can heavily suffer from non-Gaussian distributions and/or contaminated datasets, mainly because of the underlying Gaussian assumption that is not robust. To fill this gap, this paper presents a new robust discriminant analysis where each data point is drawn by its own arbitrary Elliptically Symmetrical (ES) distribution and its own arbitrary scale parameter. Such a model allows for possibly very heterogeneous, independent but non-identically distributed samples. After deriving a new decision rule, it is shown that maximum-likelihood parameter estimation and classification are very simple, fast and robust compared to state-of-the-art methods.
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Dates et versions

hal-03620144 , version 1 (25-03-2022)

Identifiants

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Pierre Houdouin, Andrew Wang, M Jonckheere, Frédéric Pascal. Robust classification with flexible discriminant analysis in heterogeneous data. 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2022), May 2022, Singapour, Singapore. ⟨10.1109/ICASSP43922.2022.9747576⟩. ⟨hal-03620144⟩
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