Year: 2022
Type: article
Abstract: Principal Component Analysis (PCA) is one of the most widely used data analysis methods in machine learning and AI. This manuscript focuses on the mathematical foundation of classical PCA and its application to a small-sample-size scenario and a large dataset in a high-dimensional space scenario. In particular, we discuss a simple method that can be used to approximate PCA in the latter case. This method can also help approximate kernel PCA or kernel PCA (KPCA) for a large-scale dataset. We hope this manuscript will give readers a solid foundation on PCA, approximate PCA, and approximate KPCA.
Source: Artificial Intelligence Review
Author: Sanparith Marukatat
Institution: National Electronics and Computer Technology Center
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,890
OpenAlex ID: W4307911024