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 ...

Marketers must be deliberate when adding dimensions to a machine learning model. The cost of adding too many is accuracy. Decluttering fever is sweeping the country thanks to Marie Kondo. But clutter ...

Principal component analysis (PCA) is a classical machine learning technique. The goal of PCA is to transform a dataset into one with fewer columns. This is called dimensionality reduction. The ...

Tuesday, October 28: Often researchers are faced with data in very high dimensions (e.g. too many predictors for a regression model), or must come up with a rule to classify data in pre-determined ...

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 ...

Principal Component Analysis from Scratch Using Singular Value Decomposition with C# Dr. James McCaffrey of Microsoft Research presents a full-code, step-by-step tutorial on a classical ML technique ...