Bilateral Sampling Randomized Singular Value Decomposition
- Resource Type
- Conference
- Authors
- Jiang, Hao; Du, Peibing; Sun, Tao; Li, Housen; Cheng, Lizhi; Yang, Canqun
- Source
- 2016 17th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) PDCAT Parallel and Distributed Computing, Applications and Technologies (PDCAT), 2016 17th International Conference on. :57-62 Dec, 2016
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Approximation algorithms
Matrix decomposition
Singular value decomposition
Computational efficiency
Manganese
Laplace equations
Low-rank
Randomized methods
Bilateral sampling
Stable
- Language
Designing fast singular value decomposition (SVD) is significantly interesting in applications. The random direct SVD (RSVD) has provided a fast scheme to compute the well-approximate SVD by unilateral randomized sampling. In this paper, we present an efficient random algorithm in a bilateral sampling way. We also prove that the proposed algorithms can be bounded well and have less computational complexity compared to RSVD when the objective matrix is approximately square. Numerical experiments on graph Laplacian and Hilbert matrix demonstrate the efficiency and stability of the proposed methods.