I want to apply a partial tucker decomposition algorithm to minimize MNIST image tensor dataset of (60000,28,28), in order to conserve its features when applying another machine algorithm afterwards like SVM. I have this code that minimizes the second and third dimension of the tensor
i = 16
j = 10
core, factors = partial_tucker(train_data_mnist, modes=[1,2],tol=10e-5, rank=[i,j])
train_datapartial_tucker = tl.tenalg.multi_mode_dot(train_data_mnist, factors,
modes=modes, transpose=True)
test_data_partial_tucker = tl.tenalg.multi_mode_dot(test_data_mnist, factors,
modes=modes, transpose=True)
How to find the best rank [i,j]
when I'm using partial_tucker
in tensorly that will give the best dimension reduction for the image while conserving as much data?
from partial tucker decomposition
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