Name
Title | ||
|---|---|---|
A Jacobi-Like Algorithm for the General Joint Diagonalization Problem with Its Application to Blind Source Separation |
Abstract | ||
|---|---|---|
The general problem of the approximate joint diagonalization of non-Hermitian matrices is considered. This problem mainly arises in the data model of the joint blind separation for two datasets. Based on a special parameterization of the two diagonalizing matrices and adapted approximations of the classical cost function, we establish a Jacobi-like algorithm. It may serve for the canonical polyadic decomposition (CPD) of a third-order tensor, and in some scenarios they can outperform traditional CPD methods. Simulation results show the competitive performance of the proposed algorithm. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/CISP-BMEI48845.2019.8965896 | 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) |
Keywords | DocType | ISBN |
nonHermitian matrices,data model,joint blind separation,matrices diagonalizing,joint diagonalization approximation,Jacobi-like algorithm,canonical polyadic decomposition,CPD,tensor | Conference | 978-1-7281-4853-3 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenrui Li | 1 | 0 | 0.34 |
| Jifei Miao | 2 | 0 | 1.69 |
| Guanghui Cheng | 3 | 0 | 0.68 |