Principal Component Analysis (PCA). [PDF]
Tunis Med, 2021 Principal component analysis (PCA) was first defined in the form that is used nowadays by Pearson (1901). He found the best-fitting line in the least squares sense to the data points, which is known today as the first principal component. Hotelling (1933) showed that the loadings for the components are the eigenvectors of the sample covariance matrix ...
Ben Salem K, Ben Abdelaziz A.
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Parameter estimation of the structured illumination pattern based on principal component analysis (PCA): PCA-SIM [PDF]
Light: Science & Applications, 2023 Principal component analysis (PCA), a common dimensionality reduction method, is introduced into SIM to identify the frequency vectors and pattern phases of the illumination pattern with precise subpixel accuracy, fast speed, and noise-robustness, which ...
Xin Chen, Yiwei Hou, Peng Xi
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Principal Component Analysis (PCA)-Supported Underfrequency Load Shedding Algorithm [PDF]
Energies, 2020 This research represents a conceptual shift in the process of introducing flexibility into power system frequency stability-related protection. The existing underfrequency load shedding (UFLS) solution, although robust and fast, has often proved to be ...
Tadej Skrjanc +2 more
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PCA of PCA: Principal Component Analysis of Partial Covering Absorption in NGC 1365 [PDF]
Monthly Notices of the Royal Astronomical Society, 2014 We analyse 400 ks of XMM-Newton data on the active galactic nucleus NGC 1365 using principal component analysis (PCA) to identify model independent spectral components.
Fabian, A. C. +3 more
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GrIP-PCA: Grassmann Iterative P-Norm Principal Component Analysis [PDF]
IEEE Open Journal of Signal Processing, 2020 Principal component analysis is one of the most commonly used methods for dimensionality reduction in signal processing. However, the most commonly used PCA formulation is based on the L2-norm, which can be highly influenced by outlier data.
Breton Minnehan +2 more
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Evaluation of stretch reflex synergies in the upper limb using principal component analysis (PCA). [PDF]
PLoS ONE, 2023 The dynamic nature of movement and muscle activation emphasizes the importance of a sound experimental design. To ensure that an experiment determines what we intend, the design must be carefully evaluated.
Frida Torell
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Introducing multiple factor analysis (MFA) as a diagnostic taxonomic tool complementing principal component analysis (PCA) [PDF]
ZooKeysMultiple factor analysis (MFA) is introduced as a diagnostic tool for taxonomy and discussed using examples from the herpetological literature. Its methodology and output are compared and contrasted to the more often used principal component analysis ...
L. Lee Grismer
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PRINCIPAL COMPONENT ANALYSIS (PCA) DAN APLIKASINYA DENGAN SPSS
Jurnal Kesehatan Masyarakat Andalas, 2009 PCA (Principal Component Analysis ) are statistical techniques applied to a single set of variables when the researcher is interested in discovering which variables in the setform coherent subset that are relativity independent of one another.Variables ...
Hermita Bus Umar
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Supercritical fluid extraction and encapsulation of Rivas (Rheum ribes) flower: Principal component analysis (PCA) [PDF]
HeliyonSupercritical CO2 modified by polar solvents can extract a wide variety of polar and non-polar chemical components compared to conventional methods. The current study aims to extract Rivas (Rheum ribes) flower using the ethanol modified supercritical CO2
Seyyed Ali Hoseini +4 more
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Identification of Dominant Air Pollutants Over Hyderabad Using Principal Component Analysis (PCA) [PDF]
Nature Environment and Pollution Technology, 2022 The study aims to bring out the interdependence of the air pollutant components through Correlation and Principal Component Analysis (PCA) to identify the sources causing air pollution in Residential, Resident cum Industrial and Industrial areas of ...
N. Vasudha and P. Venkateswara Rao
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