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I am trying to decompose a 3D matrix using python library scikit-tensor. I managed to decompose my Tensor (with dimensions 100x50x5) into three matrices. My question is how can I compose the initial matrix again using the decomposed matrix produced with Tensor factorization? I want to check if the decomposition has any meaning. My code is the following:

import logging

from scipy.io.matlab import loadmat

from sktensor import dtensor, cp_als

import numpy as np

//Set logging to DEBUG to see CP-ALS information

logging.basicConfig(level=logging.DEBUG)

T = np.ones((400, 50))

T = dtensor(T)

P, fit, itr, exectimes = cp_als(T, 10, init='random')

// how can I re-compose the Matrix T? TA = np.dot(P.U[0], P.U[1].T)

I am using the canonical decomposition as provided from the scikit-tensor library function cp_als. Also what is the expected dimensionality of the decomposed matrices?

1 Answer

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by (33.2k points)

You can simply perform a cross product of matrics. 

image

can be expressed using Einstein notation as

image

 

Code implementation of this problem using NumPy:


 

numpy.einsum('az,bz,cz,dz -> abcd', A, B, C, D)

so in your case, you would use

numpy.einsum('az,bz->ab', P.U[0], P.U[1])

or, in your 3-matrix case

numpy.einsum('az,bz,cz->abc', P.U[0], P.U[1], P.U[2])

sktensor.ktensor.ktensor also have a method totensor() that does exactly this:

np.allclose(np.einsum('az,bz->ab', P.U[0], P.U[1]), P.totensor())

>>> True

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Hope this answer helps.

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