Article

A Generalized Geometric Theory of Centroid Discriminant Analysis for Linear Classification of Multi-Dimensional Data

2025-02-11

Abstract excerpt

Linear classifiers are preferred for some tasks because they overfit less and provide interpretable decision boundaries. Yet, achieving both scalability and predictive performance remains challenging. Here, we propose a theoretical framework named geometric discriminant analysis (GDA). GDA includes the family of linear classifiers that can be expressed as function of a centroid discriminant basis (CDB0) - the conn...

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Literature Corpus work: 8b796df2-daf8-5edf-b2bb-f1b282058fed
DOI: 10.20944/preprints202502.0789.v1

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