Description Usage Arguments Value Author(s) References Examples
TTWOPT incrementaly decomposes the input tensor by gradient desecent. The tensor with missing entries is also specified with weight tensor W.
1 |
X |
The input tensor. |
Ranks |
TT-ranks to specify the lower dimensions. |
W |
The weight tensor to specify the missing entries (0: missing, 1: existing). The size must be same as that of X. |
eta |
The learning rate parameter of the gradient descent algorithm (Default : 1E-10). |
thr |
The threshold to determine the convergence (Default: 1E-10). |
num.iter |
The number of iteration (Default: 30). |
G : Core tensors RelChange : The relative change of the error f : The values of the object function RecError : The reconstruction error between data tensor and reconstructed tensor from C, U, and R
Koki Tsuyuzaki
Yuan, Longhao, et. al., (2017). Completion of high order tensor data with missing entries via tensor-train decomposition. International Conference on Neural Information Processing
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | library("rTensor")
# Tensor data
X1 <- array(rnorm(3*5*7*9*11), c(3,5,7,9,11))
dimnames(X1) <- list(
I=paste0("i", 1:3),
J=paste0("j", 1:5),
K=paste0("k", 1:7),
L=paste0("l", 1:9),
M=paste0("m", 1:11)
)
X1 <- as.tensor(X1)
# TT-ranks
Ranks <- c(p=2, q=4, r=6, s=8)
# TTWOPT
out.TTWOPT <- TTWOPT(X1, Ranks, eta=1E-7)
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