## Abstract

Tensor diagonalization means transforming a given tensor to an exactly or nearly diagonal form through multiplying the tensor by non-orthogonal invertible matrices along selected dimensions of the tensor. It has a link to an approximate joint diagonalization (AJD) of a set of matrices. In this paper, we derive (1) a new algorithm for a symmetric AJD, which is called two-sided symmetric diagonalization of an order-three tensor, (2) a similar algorithm for a non-symmetric AJD, also called a two-sided diagonalization of an order-three tensor, and (3) an algorithm for three-sided diagonalization of order-three or order-four tensors. The latter two algorithms may serve for canonical polyadic (CP) tensor decomposition, and in certain scenarios they can outperform traditional CP decomposition methods. Finally, we propose (4) similar algorithms for tensor block diagonalization, which is related to tensor block-term decomposition. The proposed algorithm can either outperform the existing block-term decomposition algorithms, or produce good initial points for their application.

Original language | English |
---|---|

Pages (from-to) | 313-320 |

Number of pages | 8 |

Journal | Signal Processing |

Volume | 138 |

DOIs | |

Publication status | Published - 1 Sep 2017 |

## Keywords

- Block-term decomposition
- Canonical polyadic decomposition
- Joint matrix diagonalization
- Multilinear models
- Parallel factor analysis