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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 J Harrison +2 more
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N-Dimensional Principal Component Analysis [PDF]
, 2010 In this paper, we first briefly introduce the multidimensional Principal Component Analysis (PCA) techniques, and then amend our previous N-dimensional PCA (ND-PCA) scheme by introducing multidirectional decomposition into ND-PCA implementation.
Yu, Hongchuan
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Novel folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing [PDF]
, 2014 As a widely used approach for feature extraction and data reduction, Principal Components Analysis (PCA) suffers from high computational cost, large memory requirement and low efficacy in dealing with large dimensional datasets such as Hyperspectral ...
Han, Junwei +6 more
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Singular Learning of Deep Multilayer Perceptrons for EEG-Based Emotion Recognition
Frontiers in Computer Science, 2021 Human emotion recognition is an important issue in human–computer interactions, and electroencephalograph (EEG) has been widely applied to emotion recognition due to its high reliability.
Weili Guo +6 more
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Canadian Journal of Statistics, 2023 AbstractMahalanobis distance of covariate means between treatment and control groups is often adopted as a balance criterion when implementing a rerandomization strategy. However, this criterion may not work well for high‐dimensional cases because it balances all orthogonalized covariates equally.
Hengtao Zhang +2 more
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Color face recognition using quaternion PCA [PDF]
, 2011 Recently, biometric systems have attracted the attention of both academic and industrial communities. Advances in hardware and software technologies have paved the way to such growing interest.
Jaha, Emad Sami, Lahouari, Ghouti
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Classification of biochemical and biomechanical data of diabetic rats treated with magnetic field by pca-supported j48 algorithm [PDF]
Kafkas Universitesi Veteriner Fakültesi Dergisi, 2019 The aim of this study was to investigate the J48 mediated decision tree algorithm from the principal component analysis - PCA, which is more complex, one of the statistical algorithms of diabetic metabolic disorders of Wistar albino rats" biochemical ...
Aykut PELİT +3 more
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A pilot study of the paraoxonase-1 (Q192 R) gene polymorphism association with prostate cancer in the Egyptian population [PDF]
Journal of Bioscience and Applied Research, 2020 Background and Objectives: Prostate cancer (PCa) is one of the most common cancer types in men and recognized as the fifth cause of death globally. Human paraoxanase-1 (PON1) is an enzyme synthesized in the liver and linked with high-density lipoprotein (
Hany Abd Al Hamid +4 more
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