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:1906.02590v2 Announce Type: replace-cross Abstract: This tutorial explains Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) as two fundamental classification methods...
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...
arXiv:2606. 29053v1 Announce Type: new Abstract: In general, an ensemble classifier is more accurate than a single classifier.
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:2609.14815v1 Announce Type: cross Abstract: This paper introduces a novel framework for Regularized Multivariate Functional Principal Component Analysis (ReMFPCA) via Functional Singular Value...
arXiv:2507.23559v2 Announce Type: replace-cross Abstract: Certain data are naturally modeled by networks or weighted graphs, be they biological networks or mobility networks. When there is no canonic...
arXiv:2606. 05584v1 Announce Type: cross Abstract: High-dimensional feature representations are widely used in machine learning-based cyberattack detection systems.
arXiv:2609.07493v1 Announce Type: new Abstract: In this paper, we propose a class-wise dimension (channel) selection framework for Multivariate Time Series Classification (MTSC). Rather than applying...
The paper explores subdomain-aware dimensionality reduction for pretrained image embeddings, applying techniques such as PCA and LDA to compress representations within specific image subdomains. Results show that this targeted compression reduces space and computational complexity while improving accuracy compared to using full embeddings. Additionally, the study demonstrates that the compressed representations retain transfer learning capabilities across tasks.
arXiv:2607. 27463v1 Announce Type: new Abstract: Dimensionality Reduction (DR) is a fundamental tool for high-dimensional data exploration, reducing the complexity of latent spaces of machine learning models, and assisting in the explanation of complex opaque models.
arXiv:2505.18918v4 Announce Type: replace-cross Abstract: Principal component analysis (PCA) is a key tool in the field of data dimensionality reduction. Various methods have been proposed to extend...
The paper investigates Partial Least Squares (PLS) in high-dimensional settings, focusing on a model where two data matrices share a low-rank latent structure plus individual-specific components. By analyzing the singular vectors of the cross‑covariance matrix with random matrix theory, the authors derive asymptotic characterizations of how well the estimated latent directions align with the true ones. They show that the PLS variant based on Singular Value Decomposition (PLS‑SVD) outperforms separate principal component analysis in detecting the common latent subspace, while also identifying regimes where PLS‑SVD behaves counter‑intuitively or reaches fundamental limits.