Abstract: Principal Component Analysis (PCA) is one of the most important unsupervised dimensionality reduction algorithms, which uses squared $\ell _{2}$ -norm to make it very sensitive to outliers.

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Non-Commercial (NC): Only non-commercial uses of the work are permitted. No ...

ABSTRACT: With the rapid development of science and technology, artificial intelligence plays a significant role across various domains. In recent years, frequent occurrences of natural disasters and ...

ABSTRACT: This study applies Principal Component Analysis (PCA) to evaluate and understand academic performance among final-year Civil Engineering students at Mbeya University of Science and ...

DUBLIN--(BUSINESS WIRE)--The "Bakery Processing Equipment Market - Equipment Types, Applications and End-Use Sectors" report has been added to ResearchAndMarkets.com's offering. Worldwide, the demand ...

The authors present a critique of current usage of principal component analysis in geometric morphometrics, making a compelling case with benchmark data that standard techniques perform poorly. The ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Mass spectrometry imaging (MSI) is constantly improving in spatial resolving power, ...

Abstract: Principal Component Analysis (PCA) aims to acquire the principal component space containing the essential structure of data, instead of being used for mining and extracting the essential ...