HDA: an iterative hyperplane-search method for discriminant analysis

Ian E. Tomeo; Panos P. Markopoulos

doi:10.1117/12.2664331

12 June 2023 HDA: an iterative hyperplane-search method for discriminant analysis

Ian E. Tomeo, Panos P. Markopoulos

Proceedings Volume 12538, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V; 125381U (2023) https://doi.org/10.1117/12.2664331
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Conference Poster

Abstract

In this paper, we introduce HDA, an iterative hyperplane-search method for Generalized Discriminant Analysis (GDA), designed to tackle challenges associated with high-dimensional data and limited sample sizes. Traditional LDA and GDA methods often struggle with poor covariance estimates, which lead to overfitting and reduced generalization capabilities. Furthermore, standard single-shot GDA incurs a high computational cost, particularly for high-dimensional data. Our proposed HDA method provides a tunable performance-complexity trade-off, enabling better model generalization by iteratively refining the discriminant analysis solution. We extensively evaluate HDA’s performance on various benchmark datasets and compare it to other GDA and LDA alternatives. The experimental results highlight the merits of HDA in terms of classification performance, balance between computational complexity, and enhanced model generalization. This study paves the way for improved discriminant analysis techniques, particularly in scenarios where data dimensionality and limited sample sizes pose significant challenges.

Citation Download Citation

Ian E. Tomeo and Panos P. Markopoulos "HDA: an iterative hyperplane-search method for discriminant analysis", Proc. SPIE 12538, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V, 125381U (12 June 2023); https://doi.org/10.1117/12.2664331

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