tucker: Tucker Decomposition
In rTensor: Tools for Tensor Analysis and Decomposition

Description Usage Arguments Details Value Note References See Also Examples

The Tucker decomposition of a tensor. Approximates a K-Tensor using a n-mode product of a core tensor (with modes specified by ranks) with orthogonal factor matrices. If there is no truncation in one of the modes, then this is the same as the MPCA, mpca. If there is no truncation in all the modes (i.e. ranks = tnsr@modes), then this is the same as the HOSVD, hosvd. This is an iterative algorithm, with two possible stopping conditions: either relative error in Frobenius norm has gotten below tol, or the max_iter number of iterations has been reached. For more details on the Tucker decomposition, consult Kolda and Bader (2009).

1	tucker(tnsr, ranks = NULL, max_iter = 25, tol = 1e-05)

`tnsr`	Tensor with K modes
`ranks`	a vector of the modes of the output core Tensor
`max_iter`	maximum number of iterations if error stays above `tol`
`tol`	relative Frobenius norm error tolerance

Uses the Alternating Least Squares (ALS) estimation procedure also known as Higher-Order Orthogonal Iteration (HOOI). Intialized using a (Truncated-)HOSVD. A progress bar is included to help monitor operations on large tensors.

a list containing the following:

Z: the core tensor, with modes specified by ranks
U: a list of orthgonal factor matrices - one for each mode, with the number of columns of the matrices given by ranks
conv: whether or not resid < tol by the last iteration
est: estimate of tnsr after compression
norm_percent: the percent of Frobenius norm explained by the approximation
fnorm_resid: the Frobenius norm of the error fnorm(est-tnsr)
all_resids: vector containing the Frobenius norm of error for all the iterations

The length of ranks must match tnsr@num_modes.

T. Kolda, B. Bader, "Tensor decomposition and applications". SIAM Applied Mathematics and Applications 2009.

hosvd, mpca

tnsr <- rand_tensor(c(4,4,4,4))
tuckerD <- tucker(tnsr,ranks=c(2,2,2,2))
tuckerD$conv 
tuckerD$norm_percent
plot(tuckerD$all_resids)

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[1] TRUE
[1] 13.5284