Tensor PCA from basis in tensor space
Abstract
The aim of this paper is to present a mathematical framework for tensor PCA. The proposed approach is able to overcome the limitations of previous methods that extract a low dimensional subspace by iteratively solving an optimization problem. The core of the proposed approach is the derivation of a basis in tensor space from a real self-adjoint tensor operator, thus reducing the problem of deriving a basis to an eigenvalue problem. Three different cases have been studied to derive: i) a basis from a self-adjoint tensor operator; ii) a rank-1 basis; iii) a basis in a subspace. In particular, the equivalence between eigenvalue equation for a real self-adjoint tensor operator and standard matrix eigenvalue equation has been proven. For all the three cases considered, a subspace approach has been adopted to derive a tensor PCA. Experiments on image datasets validate the proposed mathematical framework.
Cite
@article{arxiv.2305.02803,
title = {Tensor PCA from basis in tensor space},
author = {Claudio Turchetti and Laura Falaschetti},
journal= {arXiv preprint arXiv:2305.02803},
year = {2024}
}
Comments
This version contains a new experiment better showing the potentiality of the paper and a corrected autor list. This work has been submitted to the IEEE for possible publication