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Principal Component Analysis
This chapter first introduces the definition, theorem, and properties of the overall Principal Component Analysis (PCA), and then describes the...
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Factor Analysis
This chapter presents an overview of factor analysis in the broad sense of the term, comprising principal components analysis as well as exploratory...
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Sparse online principal component analysis for parameter estimation in factor model
Factor model has the capacity of reducing redundant information in real data analysis. Note that sparse principal component (SPC) method is developed...
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Factor Analysis and Probabilistic Principal Component Analysis
Learning models can be divided into discriminative and generative models. Discriminative models discriminate the classes of data for better...
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Factor Analysis
The explorative factor analysis is a procedure of multivariate analysis which aims at identifying structures in large sets of variables. Large sets...
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Principal Component Analysis of Localization-Delocalization Matrices
Principal Component Analysis (PCA) and one of its variants, Factor Analysis (FA), are dimensionality reduction statistical approaches that replace...
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Generalized spherical principal component analysis
Outliers contaminating data sets are a challenge to statistical estimators. Even a small fraction of outlying observations can heavily influence most...
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Principal component analysis
Principal component analysis is a versatile statistical method for reducing a cases-by-variables data table to its essential features, called...
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Monitoring groundwater quality using principal component analysis
For areas without perennial surface water sources, groundwater might be considered the second-largest source of drinking water after surface water....
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Groundwater Quality Assessment Using Principal Component and Cluster Analysis
The application of statistical techniques for the study of groundwater data provides an authentic understanding of aquifer and ecological condition...
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Hierarchical disjoint principal component analysis
Dimension reduction, by means of Principal Component Analysis (PCA), is often employed to obtain a reduced set of components preserving the largest...
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Stroke walking and balance characteristics via principal component analysis
Balance impairment is associated gait dysfunction with several quantitative spatiotemporal gait parameters in patients with stroke. However, the link...
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Principal Component Analysis for Distributions Observed by Samples in Bayes Spaces
Distributional data have recently become increasingly important for understanding processes in the geosciences, thanks to the establishment of...
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Hardware-efficient quantum principal component analysis for medical image recognition
Principal component analysis (PCA) is a widely used tool in machine learning algorithms, but it can be computationally expensive. In 2014, Lloyd,...
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Supervised feature selection using principal component analysis
The principal component analysis (PCA) is widely used in computational science branches such as computer science, pattern recognition, and machine...
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Utilizing Principal Component Analysis for the Identification of Gas Turbine Defects
This study explores the use of the nonlinear principal component analysis (NLPCA) technique for detecting gas turbine faults. The resurgence of...
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Language Corpora and Principal Components Analysis
The increase in use of statistical analyses to represent linguistic data constitutes an important turning point in the field of linguistics, allowing...
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Spike and slab Bayesian sparse principal component analysis
Sparse principal component analysis (SPCA) is a popular tool for dimensionality reduction in high-dimensional data. However, there is still a lack of...
