RLDA's misclassification rate has a deterministic finite-n approximation, revealing small-eigenvalue directions as the key structural factor, and a spectral-enhancement classifier (SEDA) exploits this with theoretical guarantees.
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Structural Effect and Spectral Enhancement of High-Dimensional Regularized Linear Discriminant Analysis
RLDA's misclassification rate has a deterministic finite-n approximation, revealing small-eigenvalue directions as the key structural factor, and a spectral-enhancement classifier (SEDA) exploits this with theoretical guarantees.