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 906 for “"principal component analysis"”.
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Generalized Principal Component Analysis
… of this dissertation is to extend the classical Principal Components Analysis (PCA), aiming to reduce the dimensionality of a large number of Normal interrelated variables, in two directions. The first is to go beyond the static (contemporaneous or synchronous) covariance matrix among these …
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Three-mode principal component analysis in designed experiments
… for decomposing multivariate data, three-mode principal component analysis, to a three-way table with one observation per cell. It is based on the class of multiplicative models for three-way tables (s x t x u) whose general form has expectation E(y<sub>ijk</sub>) = μ + α<sub>i</sub> + …
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Multivariate geostatistical simulation of compositional data using Principal Component Analysis
… modeling of compositional data using Principal Component Analysis (PCA). According to the methodology, grades are, first, transformed to log-ratios. Then, these log-ratios are linearly transformed to Principal Components (PCs). PCA tends to spatially decorrelate the factors, allowing …
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Quasi-objective Nonlinear Principal Component Analysis and applications to the atmosphere
NonLinear Principal Component Analysis (NLPCA) using three-hidden-layer feed-forward neural networks can produce solutions that over-fit the data and are non-unique. These problems have been dealt with by subjective methods during the network training. This study shows that these problems are …
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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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Learning common sense knowledge from user interaction and principal component analysis
… as a semantic network called ConceptNet. Using principal component analysis on the graph structure of ConceptNet yields AnalogySpace, a vector space representation of common sense knowledge. This representation reveals large-scale patterns in the data, while smoothing over noise, and predicts …
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Multivariate time series clustering using kernel variant multi-way principal component analysis
… may not satisfy the model validity. Multi-way Principal Component Analysis can be used for this case, but the normality assumption can restrict to handle nonlinear data such as multivariate time series with high order interactions. Kernel variant MPCA will be proposed for an alternative …
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Nonparametric Multivariate Statistical Process Control Using Principal Component Analysis And Simplicial Depth
… focusing on the dimensionality reduction using Principal Component Analysis. The proposed technique is different from current approaches given that it creates a nonparametric control chart using robust simplicial depth ranks of the first and last set of principal components to improve signal …
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Investigation of factor rotation routines in principal component analysis of stock returns
… routines that will produce uncorrelated rotated principal components for a dataset of stock returns, in an attempt to identify the macroeconomic factors that best explain the variability among risk-adjusted stock returns on the Johannesburg Stock Exchange. An alternative to the more traditional …
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Modified Kernel Principal Component Analysis and Autoencoder Approaches to Unsupervised Anomaly Detection
… existing anomaly detection algorithms, Kernel Principal Component Analysis (KPCA) and Autoencoders (AE), and proposes novel solutions to improve both of their performances in the unsupervised settings. Anomaly detection has several useful applications, such as intrusion detection, fault …
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In-situ wafer uniformity estimation using principal component analysis and function approximation methods
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.
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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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Sex-Based Differences In Lifting Technique Under Increasing Load Conditions: A Principal Component Analysis
… tasks and back strength-adjusted loads in the analysis of lifting technique. Eleven male and 14 female participants (n=25) with no previous history of low back pain participated in the study. Participants completed freestyle, symmetric lifts of a box with handles from the floor to table height …
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Principal component analysis and classification of discrete and mixed feature datasets using Gaussian copula
… a scale invariant and data contamination robust principal component analysis (PCA) for discrete datasets that was further extended to ordinal categorical datasets. This method solves the scale variant and outlier sensitive problem of the usual PCA, and enables us to perform a dimensionality …
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Integrating Principal Component Analysis and Deep Learning Methods for Data Representation and Image Denoising
… reduction and denoising with a focus on Principal Component Analysis (PCA) and its nonlinear counterparts. The main aim is to review the PCA machinery and assess its effectiveness on real data and images. Two prototype tasks and a comparative denoising study are considered. First, PCA is …
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Early Forest Fire Detection via Principal Component Analysis of Spectral and Temporal Smoke Signature
… (visible & infrared) video. By utilizing principal component analysis (PCA) followed by spatial filtering of principal component images the location of smoke can be accurately identified over a period of exposure time with a given frame capture rate. This result can be further analyzed …
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Parallel factor analysis of gait waveform data: a multimode extension of principal component analysis
… in multivariate form, so some multivariate analysis is often used to understand interrelationships between observed data. Principal Component Analysis (PCA), a data reduction technique for correlated multivariate data, has been widely applied by gait analysts to investigate patterns of …
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