standardize data before pca about multivariatestats.jl HOT 5 CLOSED

you should probably allow something like:

x = rand(3, 10)
z = fit(ZScoreTransform, x)
p = fit(PCA, transform(z, x))
transform((z, p), xnew)

or maybe even make the objects callable:

xnew |> z |>  p

and for the reconstruction:

y |> inv(p) |> inv(z)

from multivariatestats.jl.

Usually, you would standardize data prior to fitting and do not mix two operations together. It's better not to mix two operations together.

FYI, there is data transformation PR pending: JuliaStats/StatsBase.jl#85

from multivariatestats.jl.

What about the centering step, are you going to remove that?

from multivariatestats.jl.

What about the centering step, are you going to remove that?

No, not really.

Transformation and reconstructions are following:

x = rand(3, 10)
# standardize data
T = fit(ZScoreTransform, x)
xstd = transform(Z, x)
# perform PCA
M = fit(PCA, xstd)
ysub = transform(M, xstd)
# reconstruct to original space
ystd = reconstruct(M, ysub)
# reconstruct to original scale
y = reconstruct(T, ystd)
x ≈ y

Even thought pipeline X |> ZScoreTransform |> PCA looks appealing. It is beyond the scope of this package.

from multivariatestats.jl.

What about the centering step, are you going to remove that?

No, not really.

This seems like an arbitrary choice.

from multivariatestats.jl.