Abstract—We have been conducting research on
multi-dimensional data analysis. Because it is difficult to teach
students of our laboratory the concept of multidimensional data
processing, we are developing teaching materials to make them
easier to understand that. In this paper, we described a method
to solve 3-d puzzles using HOSVD, which is one of the methods
of tensor decomposition, and proposed utilizing it to education.
Specifically, we took up several kinds of 3-d puzzles and showed
their solutions and scripts of R language.
Index Terms—Multidimensional data processing, tensor decomposition, HOSVD, 3-d puzzles, understanding support.
A. Ishida is with the Faculty of Liberal Studies, Kumamoto College, National Institute of Technology, Koshi, 861-1102 Japan (e-mail: firstname.lastname@example.org).
N. Yamamoto and J. Murakami is with the Department of Human-Oriented Information Systems Engineering, Kumamoto College, National Institute of Technology, Koshi, 861-1102 Japan.
N. Oishi is with the Department of Information, Communication and Electronic Engineering, Kumamoto College, National Institute of Technology, Koshi, 861-1102 Japan.
Cite: Akio Ishida, Naoki Yamamoto, Jun Murakami, and Nobuhiro Oishi, "Solving 3-D Puzzles Using Tensor Decomposition and Application to Education of Multidimensional Data Analysis," International Journal of Machine Learning and Computing vol. 8, no. 5, pp. 447-453, 2018.