tensorly.decomposition.non_negative_parafac

non_negative_parafac(tensor, rank, n_iter_max=100, init='svd', tol=1e-06, random_state=None, verbose=0)[source]

Non-negative CP decomposition

Uses multiplicative updates, see [R8]

Parameters:	tensor : ndarray rank : int number of components n_iter_max : int maximum number of iteration init : {‘svd’, ‘random’}, optional tol : float, optional tolerance: the algorithm stops when the variation in the reconstruction error is less than the tolerance random_state : {None, int, np.random.RandomState} verbose : int, optional level of verbosity
Returns:	factors : ndarray list list of positive factors of the CP decomposition element i is of shape (tensor.shape[i], rank)

References

[R8]	(1, 2) Amnon Shashua and Tamir Hazan, “Non-negative tensor factorization with applications to statistics and computer vision”, In Proceedings of the International Conference on Machine Learning (ICML), pp 792-799, ICML, 2005