Comments (5)
I'm not sure if you have specific reasons for wanting to use VBsparsePCA (Bayesian vs. not?), but there's a different implementation of PCA for sparse data in irlba that I have used a fair amount with great results. It is what is used in widyr as well.
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I'll check it out. I saw VBsparsePCA
when it was recently released and liked that it was fast and consistent with prcomp()
results.
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One difference is that VBsparsePCA
applies the sparsity for the all the loadings for a predictor (so it has no affect on all components). irlba
doesn't use that constraint. Since I would only need to keep the rotation matrix, it might be easy to do both (step_pca_sparse()
and step_pca_sparse_bayes()
?).
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There is also a sparse pca step in the MachineShop package using the {elasticnet} package
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