TY  - INPR
ID  - SisLab4712
UR  - https://eprints.uet.vnu.edu.vn/eprints/id/eprint/4712/
A1  - Le, Trung Thanh
A1  - Karim, Abed Meraim
A1  - Adel, Hafiane
A1  - Nguyen, Linh Trung
N2  - We studied the problem of sparse subspace tracking in the
high-dimensional regime where the dimension is comparable
to or much larger than the sample size. Leveraging power iteration and thresholding methods, a new provable algorithm
called OPIT was derived for tracking the sparse principal subspace of data streams over time. We also presented a theoretical result on its convergence to verify its consistency in high dimensions. Several experiments were carried out on both synthetic and real data to demonstrate the effectiveness of OPIT.
TI  - Sparse Subspace Tracking in High Dimensions
AV  - public
M2  - Singapore
T2  - ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
ER  -