Geodesic distance between multilinear subspaces
We defined geodesic distances between multilinear subspaces for pattern recognition.
Sampled values of volumetric data are expressed as three-way array data, which is expressed as tensors.
Multi-way forms of volumetric data require quantitative methods for the discrimination of multi-way forms.
Therefore, we define geodesic measures for multilinear subspaces of multi-way data arrays using transportation between the Stiefel manifolds.
• Hayato Itoh, Atsushi Imiya: Multilinear Subspace Method Based on Geodesic Distance for Volumetric Object Classification,
In Proc. International Conference on Computer Analysis of Images and Patterns, Lecture Notes in Computer Science, vol. 11678, pp. 672-683, 2019
• Hayato Itoh, Atsushi Imiya, Tomoya Sakai: Distances Between Tensor Subspaces,
In Applications of Intelligent Systems, Frontiers in Artificial Intelligence and Applications, vol. 310, pp. 50-59, 2018