MultiCX {ccTensor} | R Documentation |
MultiCX Tensor Decomposition
Description
The input data is assumed to be a tensor. MultiCX decomposes the tensor into a core tensor and some factor matrices. The factor matrices are not estimated values but the actual column vectors sampled from the unfolded matrix in each mode.
Usage
MultiCX(Y, rank=NULL, modes=1:3, thr=0.9,
c.method=c("best.match", "random", "exact.num.random", "top.scores"))
Arguments
Y |
The input tensor (e.g. N times M times L). |
rank |
The number of low-dimension of factor matrices (e.g. J1, J2, and J3). If this argument is not specified or specified as NULL, the low-dimension is estimated based on the cumulative singular value (Default: NULL). |
modes |
The vector of the modes on whih to perform the decomposition (Default: 1:3 <all modes>). |
thr |
The threshold to determine the low-dimension of factor matrices. The value must be range 0 to 1 (Default: 0.9). |
c.method |
The column sampling algorithm (Default: best.match). |
Value
U: Core tensor (e.g. J1 times J2 times J3). C: Factor matrices (e.g. C_1: ????????) RecError : The reconstruction error between data tensor and reconstructed tensor from C and X.
Author(s)
Koki Tsuyuzaki
References
Maria F. K. B. et. al. (2019). Multidimensional CX Decomposition of Tensors. WCNPS
Examples
library("ccTensor")
library("nnTensor")
# Test data
tensdata <- toyModel(model = "CP")
# Simple usage
out <- MultiCX(tensdata, rank=c(3,4,5))