TY - GEN
T1 - Low-rank Matrix Approximation Based on Intermingled Randomized Decomposition
AU - Kaloorazi, Maboud F.
AU - Chen, Jie
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/5
Y1 - 2019/5
N2 - This work introduces a novel matrix decomposition method termed Intermingled Randomized Singular Value Decomposition (InR-SVD), along with an InR-SVD variant powered by the power iteration scheme. InR-SVD computes a low-rank approximation to an input matrix by means of random sampling techniques. Given a large and dense m × n matrix, InR-SVD constructs a low-rank approximation with a few passes over the data in O(mnk) floating-point operations, where k is much smaller than m and n. Furthermore, InR-SVD can exploit modern computational platforms and thereby being optimized for maximum efficiency. InR-SVD is applied to synthetic data as well as real data in image reconstruction and robust principal component analysis problems. Simulations show that InR-SVD outperforms existing approaches.
AB - This work introduces a novel matrix decomposition method termed Intermingled Randomized Singular Value Decomposition (InR-SVD), along with an InR-SVD variant powered by the power iteration scheme. InR-SVD computes a low-rank approximation to an input matrix by means of random sampling techniques. Given a large and dense m × n matrix, InR-SVD constructs a low-rank approximation with a few passes over the data in O(mnk) floating-point operations, where k is much smaller than m and n. Furthermore, InR-SVD can exploit modern computational platforms and thereby being optimized for maximum efficiency. InR-SVD is applied to synthetic data as well as real data in image reconstruction and robust principal component analysis problems. Simulations show that InR-SVD outperforms existing approaches.
KW - image reconstruction
KW - low-rank approximation
KW - Matrix decomposition
KW - randomized algorithms
KW - robust PCA
UR - http://www.scopus.com/inward/record.url?scp=85067342040&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2019.8683284
DO - 10.1109/ICASSP.2019.8683284
M3 - 会议稿件
AN - SCOPUS:85067342040
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 7475
EP - 7479
BT - 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
Y2 - 12 May 2019 through 17 May 2019
ER -