Abstract: Surface electromyographic signals (sEMG) usually have high-dimensional properties, and direct processing of these data consumes significant computational resources. Dimensionality reduction ...

Performance of Two-Phase Designs for the Time-to-Event Outcome and a Case Study Assessing the Relapse Risk Associated With B-ALL Subtypes This study aims to improve survival modeling in head and neck ...

MARS regression is a great and really practical technique. py-earth implemented this, based in the R earth library. The archived state of py-earth means it's only possible to get working with old ...

The advancement of tactile sensing in robotics and prosthetics is constrained by the trade-off between spatial and temporal resolution in artificial tactile sensors. To address this limitation, we ...

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 .

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.