Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 359 for “"principal component analysis (PCA)"”.
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USING PRINCIPAL COMPONENT ANALYSIS (PCA) TO OBTAIN AUXILIARY VARIABLES FOR MISSING DATA IN LARGE DATA SETS
… in large data sets. The issue can arise in any analysis in which auxiliary variables are used to inform a modern missing data handling procedure (e.g., FIML, MI) to support the missing at random assumption, reduce bias and decrease standard errors. The problem is that researchers suggest an …
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Monitoring Bacteria Cultures Using Near Infrared (nir) Binary Spectronephelometry (bsn), Raman Spectra and Principal Component Analysis (pca)
… monitor the concentrations of cells and media components that define the state of native bacterial cultures, because of changing turbidity. A new technique, binary spectronephelometry (BSN) has the same or better sensitivity and precision for population monitoring as optical density at 600nm …
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The Interaction of Intensity and Deviance on Auditory Event-Related Potentials: Findings Using Principal Component Analysis (PCA) of Current Source Densities (CSDs)
… from stimulus-specific N1/P2 processes. A CSD-PCA approach was used to identify and quantify reference-independent patterns of activity underlying the ERP. Results show that the intensity dependence of N1/P2 is largely dependent on the context in which a given intensity was cast. Namely, a high …
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A Bayesian Fusion Approach and Its Application to Integrating Audio and Visual Signals in HCI
Finally, kernel canonical correlation analysis (CCA) is developed to model nonlinear or high-order correlations between signals from two sources. Kernel CCA uses kernel principal component analysis (PCA), which elegantly combines a nonlinear transformation and linear PCA into a one-step …
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Trunk Lean in Control and Osteoarthritic Gait
… in joint angles. A method was developed using principal component analysis (PCA) to objectively reduce this inter subject variability. Gait analysis was performed on 80 subjects (40 osteoarthritis). Models were developed to define lateral thoracic tilt, as well as pelvic tilt. The trunk and …
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Distributed Singular Value Decomposition Through Least Squares
… bases. SVD has wide-ranging applications from principal component analysis (PCA) to matrix completion and approximation. Methods for computing the SVD of a matrix are extensive and involve optimization algorithms with some theoretical guarantees, though many of these techniques are not scalable …
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A model study of the interannual variability of Tanzanian rainfall
… in NCEP.Empirical Orthogonal Functions (EOF) and Principal Component Analysis (PCA) derivedfrom the GPCP precipitation and NCEP Outgoing Longwave Radiation (OLR) andgeopotential anomalies were also compared to those from the HadAM3 model.
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Factors of the term structure of sovereign yield spreads and the effect on the uncovered interest rate parity model for exchange rate prediction
Using a Principal Component Analysis (PCA) approach, we investigate the sovereign yield spread term structure of the BRICS economies against the U.S. We show that the term structure for these markets are primarily driven by three latent factors which can be classified as the spread level, slope and …
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Early Forest Fire Detection Using Texture, Blob Threshold, and Motion Analysis of Principal Components
… of the infrared (IR) video data, and (2) Principal Component Analysis of visible spectrum video data followed by motion analysis and adaptive intensity threshold. The two schemes presented are tailored to detect the fire core, and the smoke plume, respectively.</p> <p>Cooled Midwave …
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An offline multi-class auditory P300 brain-computer interface using principal and independent component analysis
… prior to classification. A combination of principal component analysis (PCA) and independent component analysis (ICA), together with a method of enhancing the P300 properties through temporal and spatial manipulation are investigated as a means of improving classification accuracy. The …
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Using Markerless Motion Capture and Principal Component Analysis to Classify BMX Freestyle Tricks
… markerless motion capture software. Next, a Principal Component Analysis (PCA) is applied to the tracking data to calculate metrics to identify each trick type. Finally, a classifier is trained to learn these metrics. The dataset used in this paper focused on three common BMX Freestyle …
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Analysis of Ambient VOCs Levels and Potential Sources in Windsor
… (CMB), Positive Matrix Factorization (PMF), and Principal Component Analysis (PCA) were applied to investigate the major sources of Windsor ambient Volatile Organic Compounds (VOCs). The annual average total VOC concentrations declined from 2005 to 2006. Summer concentrations were higher than …
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Components and principles of streaming principal components
Principal Component Analysis (PCA) is a fundamental pillar of modern data pipelines, but its traditional implementation is woefully inadequate for modern data problems. In this work we present our contributions to the field of streaming principal component analysis---research that adds critical …
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Assessing Satellite Image Data Fusion with Information Theory Metrics
… certain data fusion algorithms (pan-sharpening, principal component analysis (“PCA”) based, and high-pass filter (“HPF”) based) but not others.</p>
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Radiation source detection from mobile sensor networks using principal component analysis
… decomposition and reconstruction method based on Principal Component Analysis (PCA) is proposed to work with mobile sensor networks. Two experiments are designed to test this method's performance on real-world data. The PCA-based method's performance is analyzed using receiver operating …
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Carbon capture, utlization, and storage supply chain risk management framework development
… identified from the literature. In addition, principal component analysis (PCA) has been used to identify the communalities of the risks related to the CCUS SC functions. At the same time, 44 risk management strategies have been developed from the supply chain risk management literature in …
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Geochemical interpretation on groundwater flow in the Southern Great Basin
… for trace elements and major solutes. Cluster analysis and Principal Component Analysis (PCA), along with basic Geographical Information Systems, were used to interpret the hydrogeochemical data in the Death Valley Regional Flow System. Cluster analysis grouped the data into four major clusters …
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Semi supervised weighted maximum variance dimensionality reduction
… dimensionality reduction method of which principal component analysis (PCA) and maximum margin criterion (MMC) are special cases.. In this paper, we have extended the 2P-WMV approach from our previous work to a semi-supervised version. The objective of this work is specially to show how …
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Appearance based object recognition using independent component analysis
… done using Karhunen-Loeve transform (KLT), or principal component analysis (PCA). The lower dimensional space has been called the eigenspace. For recognition, the test image is projected likewise onto the eigenspace and its position on the appearance manifold is used for the recognition phase. …
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Genetics of the Arabidopsis Inflorescence Replacement Program
… seen for IRP phenotypes among the 85 genotypes. Principal component analysis (PCA) suggested several distinct IRP strategies within each cluster. Twelve accessions that represented each cluster were chosen and intercrossed in a partial diallel design with the ultimate aim of mapping quantitative …
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