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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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Principal Component Analysis
Principal Component Analysis (PCA) (Jolliffe, Principal component analysis. Springer, 2011) is a very well-known and fundamental linear method for...
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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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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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Convex–Concave Tensor Robust Principal Component Analysis
Tensor robust principal component analysis (TRPCA) aims at recovering the underlying low-rank clean tensor and residual sparse component from the...
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Evaluation of writing motion using principal component analysis and scaling analysis
The control of voluntary movements is a dual structure consisting of cognitive and physical controls; cognitive control, unlike physical control...
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Information theory divergences in principal component analysis
The metric learning area studies methodologies to find the most appropriate distance function for a given dataset. It was shown that dimensionality...
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Improvement of robust tensor principal component analysis based on generalized nonconvex approach
The problem of nonconvex robust tensor principal component analysis consists of recovering the low-rank and sparse part from a tensor corrupted by...
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Data Reconstruction Attack Against Principal Component Analysis
Attacking machine learning models is one of the many ways to measure the privacy of machine learning models. Therefore, studying the performance of...
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Quantum Fuzzy Principal Component Analysis
At present, principal component analysis is widely used in the dimensionality reduction processing of high-dimensional data. On the premise of...
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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...
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Efficient malware detection through inter-component communication analysis
With the development of science and technology, the number of smartphones has increased dramatically. This also exposes Android-based smartphones to...
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Innovative Lattice Sequences Based on Component by Component Construction Method for Multidimensional Sensitivity Analysis
Many challenges in the environmental protection exist since this is one of the leading priorities worldwide. Sensitivity analysis plays a...
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Entropic principal component analysis using Cauchy–Schwarz divergence
Modern pattern recognition applications are frequently associated with high-dimensional datasets. In the last decades, different approaches have been...
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Functional classwise principal component analysis: a classification framework for functional data analysis
In recent times, functional data analysis has been successfully applied in the field of high dimensional data classification. In this paper, we...
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The art of centering without centering for robust principal component analysis
Many robust variants of Principal Component Analysis remove outliers from the data and compute the principal components of the remaining data. The...
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Cauchy robust principal component analysis with applications to high-dimensional data sets
Principal component analysis (PCA) is a standard dimensionality reduction technique used in various research and applied fields. From an algorithmic...
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Underwater image enhancement by using transmission optimization and background light estimation via principal component analysis fusion
The optical properties of water exacerbate the problems that arise in underwater imaging, including low contrast, color cast, noise, and haze....
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Predictive analysis visualization component in simulated data streams
One of the most significant problems related to Big Data is their analysis with the use of various methods from the area of descriptive statistics or...
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Schatten Capped p Regularization for Robust Principle Component Analysis
Robust Principal Component Analysis (RPCA) is widely used for low-rank matrix recovery, which restores low-rank structures in damaged data through...