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- Q7049464 subject Q6960426.
- Q7049464 subject Q7144808.
- Q7049464 subject Q7849419.
- Q7049464 abstract "High-dimensional data, meaning data that requires more than two or three dimensions to represent, can be difficult to interpret. One approach to simplification is to assume that the data of interest lie on an embedded non-linear manifold within the higher-dimensional space. If the manifold is of low enough dimension, the data can be visualised in the low-dimensional space.Below is a summary of some of the important algorithms from the history of manifold learning and nonlinear dimensionality reduction (NLDR). Many of these non-linear dimensionality reduction methods are related to the linear methods listed below. Non-linear methods can be broadly classified into two groups: those that provide a mapping (either from the high-dimensional space to the low-dimensional embedding or vice versa), and those that just give a visualisation. In the context of machine learning, mapping methods may be viewed as a preliminary feature extraction step, after which pattern recognition algorithms are applied. Typically those that just give a visualisation are based on proximity data – that is, distance measurements.".
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- Q7049464 wikiPageExternalLink www.VisuMap.com.
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- Q7049464 wikiPageExternalLink GTM.
- Q7049464 wikiPageExternalLink www.nlpca.org.
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- Q7049464 wikiPageWikiLink Q7049464.
- Q7049464 wikiPageWikiLink Q7144808.
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- Q7049464 type Thing.
- Q7049464 comment "High-dimensional data, meaning data that requires more than two or three dimensions to represent, can be difficult to interpret. One approach to simplification is to assume that the data of interest lie on an embedded non-linear manifold within the higher-dimensional space.".
- Q7049464 label "Nonlinear dimensionality reduction".
- Q7049464 depiction Lle_hlle_swissroll.png.