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andland / logisticPCA

Dimensionality reduction for binary data
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Cross-validation speed #25

Open OliverPStuart opened 3 years ago

OliverPStuart commented 3 years ago

Hi,

I'm trying to evaluate the three different methods shipped with this package on my data. The data is a 76x4623 matrix.

Estimating m with cv.lpca() is extremely slow for this size matrix, just the first iteration of the function at m=1 took >24 hours. Is there any way to speed this up? For now, I am just using logisticSVD() which takes <1 minute, but I am interested to compare the different approaches.

Best, Ollie

andland commented 3 years ago

My only suggestion is to make sure you have rARPACK and set partial_decomp = TRUE.

On Sun, Jul 25, 2021 at 8:18 PM Oliver Stuart @.***> wrote:

Hi,

I'm trying to evaluate the three different methods shipped with this package on my data. The data is a 76x4623 matrix.

Estimating m with cv.lpca() is extremely slow for this size matrix, just the first iteration of the function at m=1 took >24 hours. Is there any way to speed this up? For now, I am just using logisticSVD() which takes <1 minute, but I am interested to compare the different approaches.

Best, Ollie

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