%0 Conference Paper
%A Le, Trung Thanh
%A Karim, Abed Meraim
%A Adel, Hafiane
%A Nguyen, Linh Trung
%B ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
%C Singapore
%F SisLab:4712
%T Sparse Subspace Tracking in High Dimensions
%U https://eprints.uet.vnu.edu.vn/eprints/id/eprint/4712/
%X 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.