Abstract: The central aim of this paper is to implement Deep Autoencoder and Neighborhood Components Analysis (NCA) dimensionality reduction methods in Matlab and to observe the application of these ...
The control of infinite-dimensional rigid-flexible robotic arms presents significant challenges, with direct truncation of first-order modal models resulting in poor control quality and second-order ...
SuperPCA: A Superpixelwise PCA Approach for Unsupervised Feature Extraction of Hyperspectral Imagery
Abstract: As an unsupervised dimensionality reduction method, the principal component analysis (PCA) has been widely considered as an efficient and effective preprocessing step for hyperspectral image ...
1 College of Information Science and Technology, Jinan University, Guangzhou, China. 2 University of Birmingham Joint Institute, Jinan University, Guangzhou, China. Text classification is an essential ...
GDSGE is a toolbox that solves nonlinear Dynamic Stochastic General Equilibrium (DSGE) models with a global method based on the Simultaneous Transition and Policy Function Iteration (STPFI) algorithm ...
Visual capture describes the tendency of a sound to be mislocalized to the location of a plausible visual target. This effect, also known as the ventriloquist effect, has been extensively studied in ...
On Friday August 12, after months of political debate, the US House of Representatives approved the Inflation Reduction Act of 2022—or IRA—a week after the US Senate had done the same. The IRA is a ...
Several open resource toolboxes provide feature selection algorithms to decrease redundant features, data dimensionality, and computing costs. These approaches demand programming expertise, limiting ...
Scientific theories describe observations by equations with a small number of parameters or dimensions. Memory and computational efficiency of dimension reduction procedures is a function of the size ...
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