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 ...
ABSTRACT: Bipolar disorder is a complex psychiatric condition characterized by alternating mood episodes, ranging from depression to mania. Accurate and timely detection of a patient’s current mood ...
Abstract: Traditional principal component analysis (PCA) is a common linear dimension reduction algorithm, but its dimension reduction effect is relatively poor, and the algorithm takes a long time, ...
We explore why younger women are undergoing the cosmetic procedure. By Lisa Miller Lisa Miller tells stories about how people care for themselves for the Well section. Fashion is cyclical, and so are ...
Abstract: We propose a new dimension reduction method for matrix-valued data called Matrix Non-linear PCA (MNPCA), which is a non-linear generalization of (2D) ${}^{2}$ PCA. MNPCA is based on ...
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 ...
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 (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 ...
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