Low-Rank Approximation of Matrices Via A Rank-Revealing Factorization with Randomization

Given a matrix A with numerical rank k, the two-sided orthogonal decomposition (TSOD) computes a factorization A = UDV T , where U and V are unitary, and D is (upper/lower) triangular. TSOD is rank-revealing as the middle factor D reveals the rank of A. The computation of TSOD, however, is demanding...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) S. 5815 - 5819
Hauptverfasser:	Kaloorazi, Maboud Farzaneh, Chen, Jie
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.05.2020
Schlagworte:	Approximation algorithms Image reconstruction image recovery low-rank approximation Matrix decomposition randomized numerical linear algebra rank-revealing factorization Signal processing Signal processing algorithms Speech processing Upper bound
ISSN:	2379-190X
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!