View source: R/tensor_decomp.R
tucker | R Documentation |
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 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).
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 |
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, Vol. 51, No. 3 (September 2009), pp. 455-500. URL: https://www.jstor.org/stable/25662308
hosvd
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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