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Components Analysis: Introduction and Application This chapter provides a didactic treatment of nonlinear referred to as nonlinear principal components analysis, Nonlinear complex principal component analysis : applications to tropical Pacific wind velocity anomalies Rattan, Sanjay S.P. 2004
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Neurocomputing 61 (2004) 57–70 www.elsevier.com/locate/neucom Nonlinear principal component analysis to preserve the order of principal components Abstract: Principal component analysis (PCA) is a popular tool for linear dimensionality reduction and feature extraction. Kernel PCA is the nonlinear form of PCA
Matlab toolbox for nonlinear principal component analysis (NLPCA) based on auto-associative neural networks, also known as autoencoder, replicator networks Nonlinear principal component analysis is a novel technique for multivariate data analysis, similar to the well-known method of principal component analysis. NLPCA
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Nonlinear complex principal component analysis. Overview Dimensionality reduction Principal Component Analysis Nonlinear PCA theory implementation Applications of NLPCA Lorenz attractor NH Tropospheric LFV, Principal component analysis (PCA) is an effective method of linear dimensional reduction. Because of its simplicity in theory and implementation, it is often used.
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