Abstract
The hyperbolic singular value decomposition is defined on a general n × m matrix and an m × m signature matrix pair. It is employed in finding the eigenstructure of any matrix that is expressed as the difference of two matrix outer products. Such differences arise in signal processing applications in the context of the covariance differencing. The hyperbolic SVD applies in problems where the conventional SVD cannot be employed. The existence of the hyperbolic singular value decomposition is here extended to the most general case, where neither the general matrix nor the matrix product is assumed full rank.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
