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Results tagged with dimensionality-reduction
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user 389441
Techniques for reducing a large number of variables or dimensions spanned by data to a smaller number of dimensions while preserving as much information about the data as possible. Prominent methods include PCA, Factor Analysis, MDS, Independent Component Analysis, Multiple Correspondence Analysis, Isomap, etc. The two main subclasses of techniques: feature extraction and feature selection.
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So how can I project the data with the eigenvectors from LDA?
I'm late here, but this link may be helpful to you if you're still exploring this.
Modified from Alexander Jamieson's answer there:
Mdl = fitcdiscr(X,y)
[W, LAMBDA] = eig(Mdl.BetweenSigma, Mdl.Sigma) …
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Importance of linear discriminants to classification at a given point
Many outstanding answers here detail the fundamentals of linear discriminant analysis. These include descriptions of its use in dimensionality reduction, an explanation of classification using Bayes' …