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Advances in Data Analysis and Classification

Erschienen in:

04.01.2022 | Regular Article

Optimal projections for Gaussian discriminants

verfasst von: David P. Hofmeyr, Francois Kamper, Michail C. Melonas

Erschienen in: Advances in Data Analysis and Classification | Ausgabe 1/2023

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Abstract

We study the problem of obtaining optimal projections for performing discriminant analysis with Gaussian class densities. Unlike in most existing approaches to the problem, we focus on the optimisation of the multinomial likelihood based on posterior probability estimates, which directly captures discriminability of classes. Finding optimal projections offers utility for dimension reduction and regularisation, as well as instructive visualisation for better model interpretability. Practical applications of the proposed approach show that it is highly competitive with existing Gaussian discriminant models. Code to implement the proposed method is available in the form of an R package from https://github.com/DavidHofmeyr/OPGD.

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taken from the UCI machine learning repository (Dua and Graff 2017)

We use the implementation in R’s base stats package(R Core Team 2018).

Code to implement the method is available from https://github.com/DavidHofmeyr/OPGD.

In Table 4 only values of \(p'\) up to 2 times the number of classes were considered for \(\text {OPGD}_{J}\).

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Titel: Optimal projections for Gaussian discriminants
verfasst von: David P. Hofmeyr
Francois Kamper
Michail C. Melonas
Publikationsdatum: 04.01.2022
Verlag: Springer Berlin Heidelberg
Erschienen in: Advances in Data Analysis and Classification / Ausgabe 1/2023
Print ISSN: 1862-5347
Elektronische ISSN: 1862-5355
DOI: https://doi.org/10.1007/s11634-021-00486-z

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