# tensorly.parafac2_tensor.parafac2_to_vec¶

parafac2_to_vec(parafac2_tensor)[source]

Construct a vectorized tensor from a PARAFAC2 decomposition. Uneven slices are padded by zeros.

The decomposition is on the form $$(A [B_i] C)$$ such that the i-th frontal slice, $$X_i$$, of $$X$$ is given by

$X_i = B_i diag(a_i) C^T,$

where $$diag(a_i)$$ is the diagonal matrix whose nonzero entries are equal to the $$i$$-th row of the $$I \times R$$ factor matrix $$A$$, $$B_i$$ is a $$J_i \times R$$ factor matrix such that the cross product matrix $$B_{i_1}^T B_{i_1}$$ is constant for all $$i$$, and $$C$$ is a $$K \times R$$ factor matrix. To compute this decomposition, we reformulate the expression for $$B_i$$ such that

$B_i = P_i B,$

where $$P_i$$ is a $$J_i \times R$$ orthogonal matrix and $$B$$ is a $$R \times R$$ matrix.

An alternative formulation of the PARAFAC2 decomposition is that the tensor element $$X_{ijk}$$ is given by

$X_{ijk} = \sum_{r=1}^R A_{ir} B_{ijr} C_{kr},$

with the same constraints hold for $$B_i$$ as above.

Parameters
parafac2_tensorParafac2Tensor - (weight, factors, projection_matrices)
• weights1D array of shape (rank, )

weights of the factors

• factorsList of factors of the PARAFAC2 decomposition

Contains the matrices $$A$$, $$B$$ and $$C$$ described above

• projection_matricesList of projection matrices used to create evolving

factors.

Returns
ndarray

Full constructed tensor. Uneven slices are padded with zeros.