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 ...
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 ...
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 ...
Explore the first part of our series on sleep stage classification using Python, EEG data, and powerful libraries like Sklearn and MNE. Perfect for data scientists and neuroscience enthusiasts!
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 .
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 ...
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 ...
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 ...
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