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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...
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An effective text mining framework using adaptive principle component analysis
In data mining, the medical health records can be served as a rich knowledge sources. Due to the availability of numerous medical data, the...
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Principle Component Analysis and Social Network Analysis for Decision Support of Ultra-Precision Machining
Ultra-precision machining (UPM) technology is actively engaged in manufacturing high technological products nowadays. However, as its complicated...
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Compound fault diagnosis for cooling dehumidifier based on RBF neural network improved by kernel principle component analysis and adaptive genetic algorithm
Develo** fault diagnosis for the cooling dehumidifier is very important for improving the equipment reliability and saving energy consumption. This...
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Analyzing Multiple Phenotypes Based on Principal Component Analysis
Joint analysis of multiple phenotypes can have better interpretation of complex diseases and increase statistical power to detect more significant...
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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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Component Selection
The question of when and with what benefit additive manufacturing processes can be used in an industrial context is not easy to clarify and is often...
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Spatiotemporal Analysis of Traffic Data: Correspondence Analysis with Fuzzified Variables vs. Principal Component Analysis Using Weather and Gas Price as Extra Data
Study of large rail traffic databases presents formidable challenges for transport system specialists, more particularly while kee** both space and...
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Statistical analysis of flow field variations via independent component analysis
The link between in-cylinder flow and subsequent combustion in a single-cylinder gasoline spark-ignition engine is analyzed via independent component...
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Quantification of Human Intelligence Using Principal Component Analysis
Intelligence Quotient (IQ) classifies individuals into various categories based on their cognitive abilities, and it has been used for a long period...
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Principle component analysis for nonlinear optical properties of thiophene-based metal complexes
The simulation of molecular descriptors of thiophene-based metal complexes has been performed using Gaussian 03 and Atomistic toolkit Virtual Nanolab...
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Pixel Reduction of High-Resolution Image Using Principal Component Analysis
A high-definition picture needs more storage space and occupies the memory. For a model to run efficiently, we need to provide a good-quality image,...
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Risk assessment of chemical mixtures by benchmark dose-principle component analysis approach in occupational exposure
Mixtures of organic solvents are widely used in industrial processes. Risk assessment for chemical co-exposure has always been a challenge in past...
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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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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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Dimensionality reduction beyond neural subspaces with slice tensor component analysis
Recent work has argued that large-scale neural recordings are often well described by patterns of coactivation across neurons. Yet the view that...
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Hyperspectral image classification using principle component analysis and deep convolutional neural network
Recently, Deep learning architectures based on convolutional neural networks have established outstanding performance on different image processing...
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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,...
