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 1047 for “"Principal component"”.
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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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Principal Component Regression for Construction of Wing Weight Estimation Models
… and analyzed. Even though the benefits of using principal component regression with cross validation are only demonstrated by the wing weight data fitting problem, the proposed methodology could have significant advantages in fitting other historical or hard-to-obtain data.</p>
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Principal Component Analyses of Joint Angle Curves to Examine Lifting Technique
… these effects and 2) to explore the use of principal component analysis (PCA) as a method to investigate lifting waveforms. Thirty participants (15M, 15F) completed a freestyle, symmetrical lifting protocol during which three-dimensional kinematics of the ankle, knee, hip, and lumbar and …
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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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A Principal Component Algorithm for Feedforward Active Noise and Vibration Control
A principal component least mean square (PC-LMS) adaptive algorithm is described that has considerable benefits for large control systems used to implement feedforward control of single frequency disturbances. The algorithm is a transform domain version of the multichannel filtered-x LMS algorithm. …
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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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Antarctic Station-based Pressure Reconstructions from 1905-2011 using Principal Component Regression
… Antarctic stations back to 1905, based on the Principal Component Regression (PCR). The PCR model uses only Southern Hemisphere mid-latitude pressure observations used as predictors. Several independent validation techniques are used to examine the level of accuracy of the PCR models, such as …
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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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