Somerset all rounder, Craig Overton has been Nominated for PCA Player of the Month. The PCA Player of the Month vote is now ...

BACKGROUND: Hypertension induces structural and functional damage in multiple organs. Evidence of subclinical damage ...

Abstract: Principal Component Analysis (PCA) is a fundamental data preprocessing tool in the world of machine learning. While PCA is often thought of as a dimensionality reduction method, the purpose ...

To handle principal component analysis (PCA)-based missing data with high correlation, we propose a novel imputation algorithm to impute missing values, called iterated score regression. The procedure ...

Aiming at the demagnetization fault problem of the permanent magnet synchronous motor (PMSM), a demagnetization fault diagnosis method based on the combination of the principal component analysis (PCA ...

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.

Abstract: Principal component analysis network (PCANet) is a feature learning algorithm that is widely used in face recognition and object classification. However, original PCANet still has some ...

The rotor system is a core part of rotating machinery equipment. Its safe and reliable operation directly affects the economic benefit of using the equipment and the personal safety of users. To fully ...

Automated Extraction of Tumor Staging and Diagnosis Information From Surgical Pathology Reports Prostate cancer (PCa) is among the leading causes of cancer deaths. While localized PCa has a 5-year ...

Principal Component Analysis (PCA) is an unsupervised learning algorithm known for its effectiveness in dimensionality reduction. PCA enhances data interpretability while minimising information loss ...