technologynetworks

Dimensionality Reduction in Biology: UMAP, t-SNE, PCA, and When To Use Each

Dimensionality reduction compresses thousands of gene expression measurements into a two-dimensional plot a person can actually read, and uniform manifold approximation and projection (UMAP) has ...
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 ...
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 ...
CU Boulder News & Events

Understanding Principal Component Analysis in PyTorch

PCA is an important tool for dimensionality reduction in data science and to compute grasp poses for robotic manipulation from point cloud data. PCA can also directly used within a larger machine ...

The belief that 'features with high variance are important' is a mistake. The real factors that determine machine learning model accuracy

Even if you explain 99% of the variance with PCA, your prediction accuracy might not improve by even 0.1%. This is one of the greatest tricks in statistics. Variance and predictive power are not as ...
Visual Studio Magazine

Principal Component Analysis (PCA) from Scratch Using the Classical Technique with C#

Transforming a dataset into one with fewer columns is more complicated than it might seem, explains Dr. James McCaffrey of Microsoft Research in this full-code, step-by-step machine learning tutorial.