pythonmathdata-sciencescikits

Re-compose a Tensor after tensor factorization

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?

Solution

  • The CP product of, for example, 4 matrices

    X_{abcd} = \displaystyle\sum_{z=0}^{Z}{A_{az} B_{bz} C_{cz} D_{dz} + \epsilon_{abcd}}

    can be expressed using Einstein notation as

    X_{abcd} = A_{az} B_{bz} C_{cz} D_{dz} + \epsilon_{abcd}

    or in numpy as

    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