tensorlib.decomposition.cp¶

tensorlib.decomposition.cp(X, n_components=None, tol=0.0001, max_iter=500, init_type='hosvd', random_state=None)[source]¶

CANDECOMP/PARAFAC decomposition.

Parameters:

X : ndarray Input data to decompose n_components : int The number of components in the decomposition. Note that unlike PCA or SVD, the decomposition of n_components + 1 DOES NOT contain the basis from the decomposition of n_components. tol : float, optional (default=1E-4) Stopping tolerance for reconstruction error. max_iter : int, optional (default=500) Maximum number of iterations to perform before exiting. init_type : string, optional (default=”hosvd”) How to initialize the decomposition. Choices are “random” or “hosvd”, where “random” is initialized with uniform random values, and “hosvd” is initialized by the high order SVD of the dataset. random_state : int, None, or np.RandomState instance Random seed information to use when init_type == “random”

Returns:

components : list, length = X.ndim Basis functions for X, each of shape [X.shape[idx], n_components] where idx is the index into components.

References

Kolda, T. G. & Bader, B. W.

Tensor Decompositions and Applications. SIAM Rev. 51, 455-500 (2009).

J.M. Landsberg, Tensors: Geometry and Applications. American Mathematical

Society (2011).

7. Golub and C. Van Loan. Matrix Computations, Third Edition, Chapter 5,

   Section 5.4.4, pp. 252-253.