Pseudo-Labeling

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Period ending 2026-09-14

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Pseudo-Labeling

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Semi-Supervised Learning

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Multi-Label Classification

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Unlabeled Data

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Multiclass Classification

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Weak Labels

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Linear and Quadratic Discriminant Analysis: Tutorial
Jun 1, 2019Benyamin Ghojogh, Mark CrowleyMulticlass Classification
Representing and Detecting Label Ambiguity in IMU-Based Exercise Evaluation
Date pendingAndreas Spilz, Heiko Oppel, Michael MunzStroke Rehabilitation Weak Labels
SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation
Date pendingHongjie Zhu, Xiwei Liu, Rundong Xue +5Medical Image Segmentation Semi-Supervised Learning
Are Independently Estimated View Uncertainties Comparable? Unified Routing for Trusted Multi-View Classification
Date pendingYilin Zhang, Cai Xu, Haishun Chen +2Multi-View Multiclass Classification

Jun 1, 2019 · stat.MLJ/K move · Enter open · S save

Linear and Quadratic Discriminant Analysis: Tutorial

Benyamin Ghojogh, Mark Crowley

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.

Multiclass Classification

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