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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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Principal Components-Based Node Dissimilarity Index for Complex Network Analysis
We propose a principal component analysis (PCA)-based approach to quantify (the node dissimilarity index, NDI) the extent of dissimilarity among...
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Principal Components Method in Control Charts Analysis
Shewhart control charts are successfully used to control a multi-parameter technological process, provided there is no correlation between controlled...
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One Robust Variant of the Principal Components Analysis
A new robust variant of the formulation of the problem and the method of searching for the principal components is considered. It is based on the...
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New Strategy to Hyperspectral Image Segmentation Using Principal Components Analysis
Imaging exams are fundamental in the treatment of various diseases. Through them, it is possible to obtain faster, more accurate and safer diagnoses....
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PCAnEn - Hindcasting with Analogue Ensembles of Principal Components
The focus of this study is the reconstruction of missing meteorological data at a station based on data from neighboring stations. To that end, the...
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Dimensionality study of ground motion spectra through nonlinear principal component analysis
This paper presents a study on the dimensionality reduction and feature extraction of ground motions using an artificial neural network model based...
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Extension of Fuzzy Principal Component Analysis to Type-2 Fuzzy Principal Component Analysis
This chapter compares type 1 and type 2 fuzzy principal component analysis which are based on type 1 and type 2 fuzzy C-means algorithms,...
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Action Transition Recognition Using Principal Component Analysis for Agricultural Robot Following
During harvesting using a robot, a farmer performs the actions of harvesting the crop and placing it on the robot. Meanwhile, the robot follows the...
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Application of XGBoost and kernel principal component analysis to forecast oxygen content in ESR
A model combining kernel principal component analysis (KPCA) and Xtreme Gradient Boosting (XGBoost) was introduced for forecasting the final oxygen...
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Principal component analysis and deep neural networks in modeling the melt flow index of degradable plastics
Melt flow index (MFI) is one of the physical properties of degradable plastics that must be considered in the production of well-degradable plastics....
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Principal Component-based Anomaly Detection Scheme
In this chapter, a novel anomaly detection scheme that uses a robust principal component classifier (PCC) to handle computer network...
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Interval Principal Component Analysis of Non-probabilistic Convex Model
The non-probabilistic convex model describes uncertainty of parameters in the form of a bounded set rather than the probability distribution and is...
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Points of Significance: Principal Component Analysis for Biocentric Data Visualization
Driven by resolving power of scRNA-seq analysis, multiple studies highlighted complexity and hierarchy of adipose-derived mesenchymal stem cells....
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Novel Insights on Spatio-Temporal Analysis for Frequency Modulated Thermal Wave Imaging Using Principal Component Analysis on Glass Fibre Reinforced Polymer Material
Non-Destructive Testing and Evaluation (NDT&E) is being developed across various segments of the industry for detecting the presence of defects...
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Calculation of power transformer health index using the combination of principal component analysis and mass-spring-damper model
Power transformers are one of the most important and expensive equipment in power systems which could be affected by electrical stresses such as...
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Principal Component Analysis in Noise Reduction and Beamforming
Principal component analysis (PCA) is by far the most popular and useful dimensionality reduction technique that one can find in the literature. The...
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Stock Price Prediction Using Principal Component Analysis and Linear Regression
To determine the future stock value of a company is the main purpose of stock price prediction there is a continuous change in the price of stocks...
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Decoupling analysis of stress components on monocrystalline silicon using angle-resolved oblique backscattering Raman spectroscopy
Stress is a key to controlling the electro-optical properties of semiconductor devices based on strain engineering. Micro-Raman spectroscopy is...
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Principal Components and Neural Networks Based Linear Regression to Determine Biomedical Equipment Maintenance Cost in the Peruvian Social Security Health System
In this study, multivariate linear regression models and principal component analysis, and artificial neural networks (ANN) were designed to predict...