Deep Learning with Yacine on MSN

Visualizing high-dimensional data using PCA in Scikit-Learn

Simplify complex datasets using Principal Component Analysis (PCA) in Python. Great for dimensionality reduction and ...
Frontiers

Dimension reduction and entropy-based hyperspectral image visualization using hue, saturation, and brightness

Hyperspectral imaging (HSI) captures rich spectral data across hundreds of contiguous bands for diverse applications. Dimension reduction (DR) techniques are commonly used to map the first three ...
marktechpost

Kernel Principal Component Analysis (PCA): Explained with an Example

Dimensionality reduction techniques like PCA work wonderfully when datasets are linearly separable—but they break down the moment nonlinear patterns appear. That’s exactly what happens with datasets ...
IEEE

Knowledge Learning-Based Dimensionality Reduction for Solving Large-Scale Sparse Multiobjective Optimization Problems

Abstract: Large-scale sparse multiobjective optimization problems (LSMOPs) are of great significance in the context of practical applications, such as critical node detection, feature selection, and ...
ascopubs.org

Evaluating Dimensionality Reduction for Patient-Reported Outcome–Based Survival Modeling in Patients With Head and Neck Cancer

This study aims to improve survival modeling in head and neck cancer (HNC) by integrating patient-reported outcomes (PROs) using dimensionality reduction techniques. PROs capture symptom severity ...
Scientific Research Publishing

Cook, R.D. and Li, B. (2002) Dimension Reduction for Conditional Mean in Regression. The Annals of Statistics, 30, 455-474.

ABSTRACT: We introduce the Kernel-based Partial Conditional Mean Dependence, a scalar-valued measure of conditional mean dependence of Y given X , while adjusting for the nonlinear dependence on Z .
Frontiers

Interpretability study of earthquake-induced landslide susceptibility combining dimensionality reduction and clustering

1 Electric Power Research Institute of State Grid Sichuan Electric Power Company, Chengdu, China 2 Power Internet of Things Key Laboratory of Sichuan Province, Chengdu, China An earthquake of ...
C&EN

Dimensionality Reduction Expands the Frontiers of Lanthanide Luminescence Thermometry Beyond Single-Parametric Thermal Sensing

Department of Chemistry and Biochemistry, School of Sciences and Technology, São Paulo State University (UNESP), Presidente Prudente 19060-900, Brazil Institute of Biosciences, Humanities and Exact ...
IEEE

Quick Unsupervised Hyperspectral Dimensionality Reduction for Earth Observation: A Comparison

Abstract: Dimensionality reduction can be applied to hyperspectral images so that the most useful data can be extracted and processed more quickly. This is critical in any situation in which data ...