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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EVALUATION OF DIMENSIONS OF FARMER ATTITUDES IN PRINCIPAL COMPONENT ANALYSIS (PCA) [PDF]
Annals of the Polish Association of Agricultural and Agribusiness Economists, 2020 The aim of the article was to identify leading relationship attitudes among farmers keeping animals of conservative breeds. The practical justification for the adopted analyses was to identify factors that foster desirable relations (attitudes) in ...
Marta Domagalska-Grędys
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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)-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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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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FAST-PCA: A Fast and Exact Algorithm for Distributed Principal Component Analysis
IEEE Transactions on Signal Processing, 2022 Principal Component Analysis (PCA) is a fundamental data preprocessing tool in the world of machine learning. While PCA is often thought of as a dimensionality reduction method, the purpose of PCA is actually two-fold: dimension reduction and uncorrelated feature learning.
Waheed U Bajwa, Arpita Gang
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The use of XLSTAT in conducting principal component analysis (PCA) when evaluating the relationships between sensory and quality attributes in grilled foods [PDF]
MethodsX, 2020 Charles Manful +2 more
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A holistic decision-making approach for identifying influential parameters affecting sustainable production process of canola bast fibres and predicting end-use textile choice using principal component analysis (PCA) [PDF]
Heliyon, 2021 Ikra Shuvo
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