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 ...
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 ...
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 ...
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 ...
🖼️ ️📊 lsp-python is a lightweight implementation of the Least Square Projection (LSP) dimensionality reduction technique using a sklearn style API.
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