Tutorial on PCA and approximate PCA and approximate kernel PCA
Sanparith Marukatat
Abstract
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.
Topics & Concepts
Kernel principal component analysisPrincipal component analysisComputer scienceArtificial intelligencePattern recognition (psychology)Kernel (algebra)Kernel methodSparse PCAMachine learningMathematicsSupport vector machineCombinatoricsBlind Source Separation TechniquesFace and Expression RecognitionAlgorithms and Data Compression