nonlinear principal component analysis
Spectral transformation based on nonlinear principal component analysis for dimensionality reduction of hyperspectral images
... few principal components (PCs) having the highest variance, while the remaining one contain essentially noise ...presenting nonlinear correlations between ...and nonlinear correlations in order to ...
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Nonlinear principal component analysis: An alternative method for finding patterns in environmental data.
... Non-linear PCA, as described in Gifi (1991) and more recently reviewed in Michailidis and De Leeuw (1998), approaches the task of exploring the relationships among descriptors as an optimization problem using an ...
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CEO Entrenchment and Performance: New Evidence Using Nonlinear Principal Component Analysis
... dimensional nonlinear manifold embedded in the m-dimensional space ( o : m a m ) in such a way that the output data (Yij) is obtained by minimizing the mean squared error ...
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Real-time feature extraction of P300 component using adaptive nonlinear principal component analysis
... The ability to measure and classify single-trial responses in real-time from specific brain regions has important theoretical and practical implications for both clinical and research applications. In this study, the ...
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Modified kernel principal component analysis based on local structure analysis and its application to nonlinear process fault diagnosis
... a nonlinear mapping and then to pro- ject the data along the directions of maximal variances in feature ...structure analysis technique because it does not consider the inner relationship among different ...
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Restricted kernel canonical correlation analysis
... for nonlinear data analysis with positive definite kernels ...as nonlinear extensions of linear methods: kernel principal component analysis (KPCA), Bayesian kernel methods ...
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The Attribute Optimization Method Based on the Probability Kernel Principal Component Analysis and Its Application
... a nonlinear function φ, and then Principal Component Analysis is performed in high dimensional space ...the nonlinear mapping and the decomposition of characteristic variables become ...
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Dimensionality Reduction of Image Feature Based on Mean Principal Component Analysis
... and nonlinear methods ...include Principal Component Analysis (PCA) and linear Discriminant Analysis ...the nonlinear dimensionality reduction method, the more representative is ...
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A Study of Feature Reduction Techniques and Classification for Network Anomaly Detection
... The analysis was performed on NSL-KDD dataset, with and without dimension reduction ...in nonlinear mapping, an Improved Principal Com- ponent Analysis (IPCA) method was proposed for feature ...
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Fast Iterative Kernel Principal Component Analysis
... One can do better than PCA in minimizing the reconstruction error (1) by allowing nonlin- ear projections of the data into r dimensions. Unfortunately such approaches often pose difficult nonlinear optimization ...
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A Comparative Analysis of Kernel Subspace Target Detectors for Hyperspectral Imagery
... The nonlinear versions of a number of signal process- ing techniques such as principal component analysis (PCA) [13], Fisher discriminant analysis [14], clustering in feature space ...
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A Scheme of Human Face Recognition in Complex Environments
... as Principal Component Analysis (PCA), Independent Component Analysis (ICA), Linear Discriminant Analysis (LDA), and Elastic Bunch Graph Matching (EBGM), ...
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A Kernel PCA Method for Superior Word Sense Disambiguation
... We introduce a new method for disambiguating word senses that exploits a nonlinear Kernel Prin- cipal Component Analysis (KPCA) technique to achieve accuracy superior to the best published indi- ...
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Online Tensor Robust Principal Component Analysis
... In many fields of modern data analysis, the observed data are not necessarily the prime objects of interest. The computer steering a driverless car should pay more attention to objects moving across the foreground ...
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An Eigenvalue test for spatial principal component analysis
... spatial component 1 (SP1) shows a clear differentiation of populations in the Gabon- Congo region, while SP2 detects differentiation of Central Nigerian and North Cameroonian populations, on one hand, and extreme ...
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A Review of Constrained Principal Component Analysis (CPCA) with Application on Bootstrap
... PCA was introduced in 1901 [12], it is a multivariate technique that analyzes a data in which observations are described by several inter correlated quantitative dependent variables. Its goal is to get the important ...
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Sources Affecting PM2 5 Concentrations at a Rural Semi Arid Coastal Site in South Texas
... CPF analysis of traffic emission source appor- tioned by both PMF2 and PCA/APCS at CAMS 314 showed the influence of the northeast and northwest wind ...CPF analysis (Figure 4) the dominant wind sector was ...
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Network Intrusion detection by using PCA via SMO-SVM
... Meanwhile, Abraham et al. [11] illustrated that ensemble Decision Tree was suitable for Normal, LGP for Probe, DoS and R2L and Fuzzy classifier was for R2L. Abraham et al. [12] also demonstrated the ability of their ...
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Convex Formulations for Fair Principal Component Analysis
... sured over a week via an accelerometer. In this example, we consider age to be our protected variable, specifically whether an individual is above or below 40 years of age. We exclude weekends from our analysis, ...
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Principal Component Analysis of Volatility Smiles and Skews
... Several principal component models of volatility smiles and skews have been based on daily changes in implied volatilities, by strike and/or by moneyness. Derman and Kamal (1997) analyze S&P500 and ...
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