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Tutorial on PCA and approximate PCA and approximate kernel PCA
Artificial Intelligence Review, 2022 AbstractPrincipal 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.
Sanparith Marukatat
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PCA of waveforms and functional PCA: A primer for biomechanics [PDF]
Journal of Biomechanics, 2021 Principal components analysis (PCA) of waveforms and functional PCA (fPCA) are statistical approaches used to explore patterns of variability in biomechanical curve data, with fPCA being an accepted statistical method grounded within the functional data analysis (FDA) statistical framework.
Andrew Harrison +2 more
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Some of the next articles are maybe not open access.
Forecasting crude oil prices: A scaled PCA approach
Energy Economics, 2021Yudong Wang, Yaojie Zhang, Mengxi He
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Scaled PCA: A New Approach to Dimension Reduction
Management Science, 2022Guofu Zhou, Fuwei Jiang, Dashan Huang
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Adaptive process monitoring using efficient recursive PCA and moving window PCA algorithms
Journal of the Taiwan Institute of Chemical Engineers, 2010Jyh-Cheng Jeng
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Convex Sparse PCA for Unsupervised Feature Learning
ACM Transactions on Knowledge Discovery From Data, 2017Chengqi Zhang, Yi Yang, Feiping Nie
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