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 ...
Nature

A comparative study of manifold learning methods for scRNA-seq with a trajectory-aware metric

Single-cell RNA sequencing (scRNA-seq) enables detailed analysis of cellular diversity, but the data’s high dimensionality presents analytical challenges. We compare four dimensionality reduction ...
ascopubs.org

Evaluating Dimensionality Reduction for Patient-Reported Outcome–Based Survival Modeling ...

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 ...
Nature

Enhancing feature learning of hyperspectral imaging using shallow autoencoder by adding ...

Hyperspectral Imaging (HSI) is widely used in remote sensing and medical images. These are the images that measure continuous spectral bands. They contain dozens to hundreds of spectral bands. A ...