Spectral features from frequency and time-frequency domains enable traditional ML to match or exceed attention-based DL performance in EEG disease diagnosis on small datasets, with attention unable to capture stable spectral signatures.
Linear and Quadratic Discriminant Analysis: Tutorial
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abstract
This tutorial explains Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) as two fundamental classification methods in statistical and probabilistic learning. We start with the optimization of decision boundary on which the posteriors are equal. Then, LDA and QDA are derived for binary and multiple classes. The estimation of parameters in LDA and QDA are also covered. Then, we explain how LDA and QDA are related to metric learning, kernel principal component analysis, Mahalanobis distance, logistic regression, Bayes optimal classifier, Gaussian naive Bayes, and likelihood ratio test. We also prove that LDA and Fisher discriminant analysis are equivalent. We finally clarify some of the theoretical concepts with simulations we provide.
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Vector boson fusion processes at the LHC distinguish Higgs portal dark matter from neutralino dark matter at over 5 sigma using jet kinematics and a Kolmogorov-Smirnov test with linear discriminant analysis.
Nearest Centroid recovers WHO fertility labels from the same semen parameters that define those labels at 94.2% accuracy on the 85-sample VISEM set.
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Spectral Priors vs. Attention: Investigating the Utility of Attention Mechanisms in EEG-Based Diagnosis
Spectral features from frequency and time-frequency domains enable traditional ML to match or exceed attention-based DL performance in EEG disease diagnosis on small datasets, with attention unable to capture stable spectral signatures.
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Distinguishing Higgs portal and neutralino dark matter via vector boson fusion
Vector boson fusion processes at the LHC distinguish Higgs portal dark matter from neutralino dark matter at over 5 sigma using jet kinematics and a Kolmogorov-Smirnov test with linear discriminant analysis.
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Predicting Male Fertility Using Machine Learning: A Semen Parameters Based Analysis with the VISEM Dataset
Nearest Centroid recovers WHO fertility labels from the same semen parameters that define those labels at 94.2% accuracy on the 85-sample VISEM set.