TTWOPT: Tensor-Train Decomposition by Tensor-train Weighted...

In rikenbit/ttTensor: Tensor-Train Decomposition

Tensor-Train Decomposition by Tensor-train Weighted OPTimization

Description

TTWOPT incrementaly decomposes the input tensor by gradient desecent. The tensor with missing entries is also specified with weight tensor W.

Usage

TTWOPT(X, Ranks, W=NULL, eta=1E-7, thr=1E-10, num.iter=100)

Arguments

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).

Value

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

Author(s)

Koki Tsuyuzaki

References

Yuan, Longhao, et. al., (2017). Completion of high order tensor data with missing entries via tensor-train decomposition. International Conference on Neural Information Processing

Examples

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)

rikenbit/ttTensor documentation built on Feb. 2, 2023, 4:32 a.m.