The functional principal components analysis joins the advantages of the principal components analysis and provide analysis of dynamic data. The main difference in both methods is the type of data the PCA is based on multivariate data, whereas the FPCA ...
Mirosława Sztemberg-Lewandowska
doaj
Principal component analysis of 3-dimensional facial soft-tissue morphology in three adult populations. [PDF]
Kau CH, Borbely P, Zhurov A, Oguntoba J.
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Reduction of the space dimension of parameters characterizing geomagnetic storms during the Solar Cycle 24. [PDF]
Siluszyk A +5 more
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Ascertainment Bias in Cattle SNP Arrays and Implications for Multibreed Genomic Predictions. [PDF]
Warburton CL, Hayes BJ.
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Data-driven denoising in spinal cord fMRI with principal component analysis. [PDF]
Hemmerling KJ +4 more
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Prediction of SCA Scores in Specialty Coffee Using Machine Learning. [PDF]
Ferraz GR +4 more
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Proton Nuclear Magnetic Resonance With Time-Frequency Analysis: A Potential Diagnostic Approach for Keloids. [PDF]
Xia G +10 more
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Autoencoders reveal polyunsaturated fatty acids (PUFA)-Related metabolic signature linked to cancer risk. [PDF]
Breeur M +32 more
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Dimensionality reduction of COSMO-RS molecular descriptor using functional principal component analysis (FPCA) for organic solvent mapping. [PDF]
Ramirez Cardenas LE +4 more
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Accurate characterization of mix plastic waste using ATR-FTIR spectroscopy and machine learning methods. [PDF]
Zhou Z, Shao H, Liu B, Xie Y, Wang W.
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