Visual Studio Magazine

Anomaly Detection Using Principal Component Analysis (PCA)

A good way to see where this article is headed is to take a look at the screen shot of a demo program shown in Figure 1. The demo sets up a dummy dataset of six items: [ 5.1 3.5 1.4 0.2] [ 5.4 3.9 1.7 ...
Deep Learning with Yacine on MSN

Visualizing High-Dimensional Data Using PCA in Scikit-Learn

Simplify complex datasets using Principal Component Analysis (PCA) in Python. Great for dimensionality reduction and ...
Visual Studio Magazine

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

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 ...
EurekAlert!

Principal component analysis enhances 3D super-resolution microscopy

Inside living cells, mitochondria divide, lysosomes travel, and synaptic vesicles pulse—all in three dimensions (3Ds) and constant motion. Capturing these events with clarity is vital not just for ...
ascopubs.org

Prognostic impact of PSA nadir (n) ≥0.1 ng/mL within 6 months (m) after completion of radiotherapy (RT) for localized prostate cancer (PCa): An individual patient-data (IPD ...

This is an ASCO Meeting Abstract from the 2023 ASCO Annual Meeting I. This abstract does not include a full text component.