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 ...

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

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 ...

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 ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. EncoderMap is a dimensionality reduction method that is tailored for the analysis of ...

Is your feature request related to a problem? Please describe. We wish to use cuML to accelerate our experiment on Identifying gene expression programs of cell-type identity and cellular activity.

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: In this paper, the Isometric Mapping (ISOMAP) algorithm is applied to recognize oracle bone inscription images. First, the sample set undergoes denoising and size normalization as ...