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.
By Carolina Bento
arXiv:2502. 00168v5 Announce Type: replace-cross Abstract: Supervised dimensionality reduction maps labeled data into a low-dimensional feature space while preserving class separation.
By Daniel Herrera-Esposito, Johannes Burge
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.
By Jia Li, Lin Lin
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.
By \'Abrah\'am Papp, Botond Szil\'agyi, Edith Alice Kov\'acs
arXiv:2606. 29053v1 Announce Type: new Abstract: In general, an ensemble classifier is more accurate than a single classifier.
By Donghwan Kim, Seung Hwan Park, Jun-Geol Baek
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.
By Gr\'egoire Sergeant-Perthuis (CQSB, Sorbonne Universit\'e), Elias Tsigaridas (Ouragan Team, INRIA), Jules Tsukahara (Ouragan Team, INRIA)
arXiv:2609.10334v1 Announce Type: new
Abstract: This paper addresses the issue of supervised classification in the context of hyperspectral satellite images. It deals with two fundamental aspects: di...
By Mohamed Cherifi, Ammar Mesloub, Mohammed Nabil El Korso, Tayeb Touhami, Abdennour Hacine Gharbi
arXiv:2608. 03706v1 Announce Type: new Abstract: Statistical learning is a fascinating field that has long been the mainstream of machine learning/artificial intelligence.
By Congwei Song
arXiv:2605. 03283v2 Announce Type: replace-cross Abstract: We provide a unified theoretical analysis of Linear Discriminant Analysis with simultaneous multilabel scatter matrix formulations and Stiefel orthogonality constraints.
By Brian Keith-Norambuena, Juan Bekios-Calfa
The paper introduces a method for optimizing bin boundaries in multi-dimensional discriminants using a Gaussian Mixture Model (GMM), allowing flexible definition of analysis categories. Two optimization strategies—differentiable and Bayesian—are compared in toy binary and three-class setups, with the differentiable approach excelling in multi-dimensional cases. Applied to the FAIR Universe $H
ightarrow au au$ dataset, the GMM-based optimization achieves the highest signal significance, and the tools are released as lightweight Python plugins.
By Johannes Erdmann, Nitish Kumar Kasaraguppe, Florian Mausolf
arXiv:2605. 18662v2 Announce Type: replace Abstract: Noise-tolerant PAC learning of linear models has been of central interests in machine learning community since the last century.
By Rita Adhikari, Shiwei Zeng
The paper introduces a framework that learns the kernel used in kernel methods through alignment, leveraging the Collaborative Learning and Inference (CLaI) approach. It demonstrates that CLaI can be interpreted as a kernel alignment process and that its inference stage is equivalent to kernel Bayes classification with Parzen-window density estimation. By replacing cosine similarity with a learned Mahalanobis distance, the authors extend CLaI to multiclass classification, achieving higher accuracy, faster convergence, and lower calibration error on datasets such as CIFAR-10, PathMNIST, and SleepEDF, while also showing connections to Gaussian processes and competitive calibration in sepsis prediction.
By Hollan Haule, Alfredo Gonzalez-Sulser, Javier Escudero