tensorly.decomposition.non_negative_tucker
- non_negative_tucker(tensor, rank, n_iter_max=10, init='svd', tol=0.0001, random_state=None, verbose=False, return_errors=False, normalize_factors=False)[source]
Non-negative Tucker decomposition
Iterative multiplicative update, see [2]
- Parameters:
- tensor
ndarray - rankNone, int or int list
size of the core tensor,
(len(ranks) == tensor.ndim)if int, the same rank is used for all modes- n_iter_maxint
maximum number of iteration
- init{‘svd’, ‘random’}
- random_state{None, int, np.random.RandomState}
- verboseint , optional
level of verbosity
- return_errorsboolean
Indicates whether the algorithm should return all reconstruction errors and computation time of each iteration or not Default: False
- normalize_factorsif True, aggregates the norms of the factors in the core.
- tensor
- Returns:
- corendarray
positive core of the Tucker decomposition has shape ranks
- factorsndarray list
list of factors of the CP decomposition element i is of shape
(tensor.shape[i], rank)
References
[2]Yong-Deok Kim and Seungjin Choi, “Non-negative tucker decomposition”, IEEE Conference on Computer Vision and Pattern Recognition s(CVPR), pp 1-8, 2007