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Results tagged with pca
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user 195414
Principal component analysis (PCA) is a linear dimensionality reduction technique. It reduces a multivariate dataset to a smaller set of constructed variables preserving as much information (as much variance) as possible. These variables, called principal components, are linear combinations of the input variables.
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Combining PLS-DA with PCA dimension reduction
I do PCA analysis on the dataset and find that there are only 187 non-zero eigenvalues. … My concern here arises because both PLS and PCA are used for dimension reduction I am not sure if I am doing something wrong by mixing these techniques. …
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