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 ...
Nature

Dimensionality reduction of independent influence factors in the objective evaluation of quality of experience

Big Data analytics and Artificial Intelligence (AI) technologies have become the focus of recent research due to the large amount of data. Dimensionality reduction techniques are recognized as an ...
ascopubs.org

Evaluating Dimensionality Reduction for Patient-Reported Outcome–Based Survival Modeling in Patients With Head and Neck Cancer

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 ...
VentureBeat

Understanding dimensionality reduction in machine learning models

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Machine learning algorithms have gained fame for being able to ferret out ...
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 ...