Nature

Dimensionality reduction in hyperspectral imaging using standard deviation-based band selection for efficient classification

Hyperspectral imaging generates vast amounts of data containing spatial and spectral information. Dimensionality reduction methods can reduce data size while preserving essential spectral features and ...
Nature

A tied-weight autoencoder for the linear dimensionality reduction of sample data

Dimensionality reduction is a method used in machine learning and data science to reduce the dimensions in a dataset. While linear methods are generally less effective at dimensionality reduction than ...
Visual Studio Magazine

Data Dimensionality Reduction Using a Neural Autoencoder with C#

Dr. James McCaffrey of Microsoft Research presents a full-code, step-by-step tutorial on creating an approximation of a dataset that has fewer columns. Imagine that you have a dataset that has many ...