Sparse Subspace Tracking in High Dimensions

Le Trung Thanh; Karim Abed-Meraim; Adel Hafiane; Nguyen Linh Trung

ICASSP 2022accepted0 citations

Sparse Subspace Tracking in High Dimensions

Le Trung Thanh, Karim Abed-Meraim, Adel Hafiane, Nguyen Linh Trung

Abstract

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.

BibTeX

@inproceedings{icassp2022_sparsesubspacetr,
  title = {Sparse Subspace Tracking in High Dimensions},
  author = {Le Trung Thanh and Karim Abed-Meraim and Adel Hafiane and Nguyen Linh Trung},
  booktitle = {ICASSP 2022},
  year = {2022}
}