LDA-GO uses scalable gradient optimization on low-rank precision matrices with data-driven loss selection for high-dimensional LDA, claiming Bayes optimality and finite-sample error bounds.
Fisher discriminant analysis with kernels, in: Proceedings of the 1999 IEEE Signal Processing Society Workshop
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Linear Discriminant Analysis with Gradient Optimization
LDA-GO uses scalable gradient optimization on low-rank precision matrices with data-driven loss selection for high-dimensional LDA, claiming Bayes optimality and finite-sample error bounds.