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Principal components analysis (PCA)
Computers and Geosciences, 1993Principal 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.
Waldemar Ratajczak
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Approximations of the standard principal components analysis and kernel PCA
Expert Systems With Applications, 2010Principal component analysis (PCA) is a powerful technique for extracting structure from possibly high-dimensional data sets, while kernel PCA (KPCA) is the application of PCA in a kernel-defined feature space. For standard PCA and KPCA, if the size of dataset is large, it will need a very large memory to store kernel matrix and a lot of time to ...
Wenjian Wang
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Principal Component Analysis (PCA)
2021Principal 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 ...
B. K. Tripathy +2 more
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PCA-Pruner: Filter pruning by principal component analysis
Journal of Intelligent & Fuzzy Systems, 2022Deep 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 ...
Wei Zhang, Zhiming Wang
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A principal component analysis (PCA) based assessment of the gait performance
Biomedical Engineering / Biomedizinische Technik, 2021Abstract The gait assessment is instrumental for evaluating the efficiency of rehabilitation of persons with a motor impairment of the lower extremities. The protocol for quantifying the gait performance needs to be simple and easy to implement; therefore, a wearable system and user-friendly computer program are preferable.
Gavrilović, Marija, Popović, Dejan B.
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The use of principal component analysis (PCA) to characterize beef
Meat Science, 2000Principal component analysis was performed to study the relationships between chemical, physical and sensory variables (n=18) measured on longissimus thoracis et lumborum of 79 young bulls from the following ethnic groups: hypertrophied Piemontese, normal Piemontese, Friesian, crossbred hypertrophied Piemontese×Friesian, Belgian Blue and White.
G, Destefanis +3 more
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Real-time PCA (principal component analysis) implementation on DSP
2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541), 2005PCA (principal component analysis) is a wellknown statistical technique used in many signal processing applications. An on-line temporal PCA learning algorithm is implemented on a floating-point DSP for real-time applications. This algorithm is coded in assembly language to optimize. The experimental results showed that the implemented on-line temporal
Dongho Han +3 more
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