Include PCA transform #3
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juliohm
reviewed
Oct 27, 2021
src/transforms/pca.jl
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| The PCA transform returns a table with covariance matrix | ||
| having the properties: cov(Xᵢ, Xⱼ) = 0, for i != j, and | ||
| 0 ≤ cov(Xᵢ, Xᵢ) ≤ 1, for i == j. |
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I think it doesn't force the diagonal to be in [0,1]:
We can just say something like: The PCA transform returns a table with uncorrelated variables.
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The PCA is just a rotation, it doesn't shrink the axes of the ellipsoid. The SDS and SDR do shrink the axes (i.e. diagonal of covariance).
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That's right, I'll update the description in a minute
juliohm
reviewed
Oct 27, 2021
src/transforms/pca.jl
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| 𝒯 = (; zip(names, eachcol(Y))...) | ||
| newtable = 𝒯 |> Tables.materializer(table) | ||
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| newtable, inv(V) |
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What is the size of V? Shouldn't we return the transpose(V) instead? Inverses are much more expensive to compute in general.
juliohm
approved these changes
Oct 27, 2021
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Try to add PCA transform