Robust classification with flexible discriminant analysis in heterogeneous data

• URL: http://arxiv.org/abs/2201.02967v1
• Date: Sun, 9 Jan 2022 09:22:56 GMT
• Title: Robust classification with flexible discriminant analysis in heterogeneous data
• Authors: Pierre Houdouin, Fr\'ed\'eric Pascal, Matthieu Jonckheere, Andrew Wang
• Abstract summary: This paper presents a new robust discriminant analysis where each data point is drawn by its own arbitrary scale parameter. It is shown that maximum-likelihood parameter estimation and classification are very simple, fast and robust compared to state-of-the-art methods.
• Score: 0.7646713951724009
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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