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Sensory Characterization of Cookies With Sugar Replacement by Sweeteners Using the Check-All-That-Apply (CATA) Method. [PDF]

J Food Sci
Lima IHO, Brito NLH, Cardoso FAR, Fuchs RHB.   +3 more
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Principal Component Analysis (PCA)

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 ...
Kim-Anh Lê Cao, Zoe Marie Welham
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Principal components analysis (PCA)

Computers & Geosciences, 1993
Principal Components Analysis (PCA) as a method of multivariate statistics was created before the Second World War. However, the wider application of this method only occurred in the 1960s, during the “Quantitative Revolution” in the Natural and Social Sciences.
Andrzej Maćkiewicz, Waldemar Ratajczak
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PCA-Pruner: Filter pruning by principal component analysis

Journal of Intelligent & Fuzzy Systems, 2022
Deep Convolutional Neural Networks (CNNs) have been widely used in various domains due to their outstanding performance. However, they simultaneously bring enormous computational overhead, making it difficult to deploy to mobile and edge devices. Therefore, researchers use network compression techniques such as quantization, knowledge distillation and ...
Zhang, Wei, Wang, Zhiming
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Image authenticity implementing Principal Component Analysis (PCA)

2013 10th International Conference and Expo on Emerging Technologies for a Smarter World (CEWIT), 2013
The paper addresses the application of finding key features within an image utilizing the process termed the Principal Components Analysis (PCA). Understanding this technique is critical for researchers within biometric fields and the larger cyber security field. Research, found in ASEE 2011 Conference Proceedings, titled “Edge Detectors in Engineering
Suzanna Schmeelk, John Schmeelk
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Principal Component Analysis (PCA) in Ichnology

2021
I observed the ichnofabric variable on 600 ichnofabric units in the Samarinda area of Kutai Basin. Five ichnofabric variables are bioturbation index (BI), biodiversity (ID), number of behaviors (NB), penetration depth (PD), and burrow diameter (DM) that perform as a semi-quantitative form. It must process the data with principal component analysis (PCA)
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Principal Component Analysis (PCA)

2019
Principal component analysis (PCA) is an essential algorithm in machine learning. It is a mathematical method for evaluating the principal components of a dataset. The principal components are a set of vectors in high-dimensional space that capture the variance (i.e., spread) or variability of the feature space.
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