NLPCA - nonlinear PCA - auto-associative neural networks - autoencoder bottleneck neural networks - Matthias Scholz
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FEM - Fondazione Edmund Mach. Nutrigenomics, Gut Microbiome and Human Health. University of Trento, Italy. IASMA Research and Innovation Centre.
At the end of this article, Matlab source code is provided for demonstration purposes. In an earlier article, we discussed the so called. A geometric interpretation of the covariance matrix.
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Matthias Scholz
Frankfurter Allee 128
Berlin, Germany, 10365
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I observed that nlpca.org is employing the Apache/2.4.33 (Unix) server.TITLE
NLPCA - nonlinear PCA - auto-associative neural networks - autoencoder bottleneck neural networks - Matthias ScholzDESCRIPTION
Nonlinear principal component analysis NLPCA based on auto-associative neural networks, also known as autoencoder, replicator networks, bottleneck or sandglass type networks.CONTENT
This domain has the following on the web site, "Nonlinear principal component analysis NLPCA is commonly seen as a nonlinear generalization of standard principal component analysis PCA." Our analyzers observed that the web site also said " It generalizes the principal components from straight lines to curves nonlinear." The Website also stated " Thus, the subspace in the original data space which is described by all nonlinear components is also curved." The website's header had nonlinear principal component analysis as the most important keyword. It was followed by NLPCA, nonlinear PCA, and non-linear PCA which isn't as highly ranked as nonlinear principal component analysis. The next words the site uses is circular PCA. principal component analysis was also included and will not be viewed by web crawlers.OTHER WEB PAGES
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