arXiv Machine Learning
Linear and Quadratic Discriminant Analysis: Tutorial
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
Towards Data Science
Sep 6
Linear Discriminant Analysis (LDA) in Real-Life: Dimensionality Reduction in a Real-Estate Dataset
The article discusses applying Linear Discriminant Analysis (LDA) to reduce dimensionality in a real‑estate dataset for classification tasks. It explains how LDA can transform high‑dimensional data into a lower‑dimensional space while preserving class separability. The post demonstrates the practical use of LDA in a real‑life scenario, specifically within the real‑estate domain.
arXiv Machine Learning
Jun 29
Supervised Quadratic Feature Analysis: Information Geometry Approach for Dimensionality Reduction
arXiv:2502. 00168v5 Announce Type: replace-cross Abstract: Supervised dimensionality reduction maps labeled data into a low-dimensional feature space while preserving class separation.
arXiv AI
Jun 16
Canonical Variates in Wasserstein Metric Space
arXiv:2405. 15768v2 Announce Type: replace-cross Abstract: In this paper, we address the classification of instances represented by distributions on a vector space rather than single points.
arXiv Machine Learning
Sep 22
On Generalized Naive Bayes with Continuous Features
The paper extends the Generalized Naive Bayes (GNB) model to handle continuous explanatory variables. It shows that GNB structure learning depends only on pair copulas of bivariate marginals and can be framed as a matroid, enabling greedy algorithms that minimize Kullback–Leibler divergence. Three model variants are explored—joint Gaussian, Gaussian copula with arbitrary marginals, and fully arbitrary copula and marginals—along with a GNB forest-based model reduction method and empirical comparisons to classical glass‑box classifiers.
arXiv Machine Learning
Jun 30
A Kernel Fisher Discriminant Analysis-Based Tree Ensemble Classifier: KFDA Forest
arXiv:2606. 29053v1 Announce Type: new Abstract: In general, an ensemble classifier is more accurate than a single classifier.
arXiv Machine Learning
Aug 21
Exact Algebraic Computation of Learning Coefficients for Two-Dimensional Singular Models
arXiv:2608. 20183v1 Announce Type: new Abstract: Classical information criteria such as the Bayesian Information Criterion (BIC) rely on regularity assumptions that break down for singular models, leading to incorrect model selection in settings such as deep learning.