@article{LI2007, 
author = {Xilin LI and Xianda ZHANG and Peisheng LI},
title = {Gradient Search Non-Orthogonal Approximate Joint Diagonalization Algorithm},
year = {2007},
journal = {Tsinghua Science and Technology},
volume = {12},
number = {6},
pages = {669-673},
keywords = {approximate joint diagonalization, gradient methods, decorrelation, statistics, adaptive signal processing},
url = {https://www.sciopen.com/article/10.1016/S1007-0214(07)70173-6},
doi = {10.1016/S1007-0214(07)70173-6},
abstract = {The problem of approximate joint diagonalization of a set of matrices is instrumental in numerous statistical signal processing applications. This paper describes a relative gradient non-orthogonal approximate joint diagonalization (AJD) algorithm based on a non-least squares AJD criterion and a special AJD using a non-square diagonalizing matrix and an AJD method for ill-conditioned matrices. Simulation results demonstrate the better performance of the relative gradient AJD algorithm compared with the conventional least squares (LS) criteria based gradient-type AJD algorithms. The algorithm is attractive for practical applications since it is simple and efficient.}
}