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 ...

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 ...

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 ...

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 ...