Robust classification with flexible discriminant analysis in heterogeneous data

Pierre Houdouin; Frédéric Pascal; Matthieu Jonckheere; Andrew Wang

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arXiv · 2201.02967

Robust classification with flexible discriminant analysis in heterogeneous data

Pierre Houdouin·Frédéric Pascal·Matthieu Jonckheere·Andrew Wang

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Abstract

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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Pierre Houdouin, Frédéric Pascal, Matthieu Jonckheere, Andrew Wang. 2022-01-09. Robust classification with flexible discriminant analysis in heterogeneous data. https://arxiv.org/abs/2201.02967

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