tensorly.contrib.sparse.decomposition.partial_tucker¶

partial_tucker
(tensor, modes, rank=None, n_iter_max=100, init='svd', tol=0.0001, svd='numpy_svd', random_state=None, verbose=False, mask=None)¶ Partial tucker decomposition via Higher Order Orthogonal Iteration (HOI)
Decomposes tensor into a Tucker decomposition exclusively along the provided modes.
 Parameters
 tensorndarray
 modesint list
list of the modes on which to perform the decomposition
 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’}, or TuckerTensor optional
if a TuckerTensor is provided, this is used for initialization
 svdstr, default is ‘numpy_svd’
function to use to compute the SVD, acceptable values in tensorly.SVD_FUNS
 tolfloat, optional
tolerance: the algorithm stops when the variation in the reconstruction error is less than the tolerance
 random_state{None, int, np.random.RandomState}
 verboseint, optional
level of verbosity
 maskndarray
array of booleans with the same shape as
tensor
should be 0 where the values are missing and 1 everywhere else. Note: if tensor is sparse, then mask should also be sparse with a fill value of 1 (or True).
 Returns
 corendarray
core tensor of the Tucker decomposition
 factorsndarray list
list of factors of the Tucker decomposition. with
core.shape[i] == (tensor.shape[i], ranks[i]) for i in modes