relation: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/4712/
title: Sparse Subspace Tracking in High Dimensions
creator: Le, Trung Thanh
creator: Karim, Abed Meraim
creator: Adel, Hafiane
creator: Nguyen, Linh Trung
subject: Electronics and Communications
subject: ISI/Scopus indexed conference
description: 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.
type: Conference or Workshop Item
type: PeerReviewed
format: application/pdf
language: en
identifier: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/4712/1/ICASSP2022_published.pdf
identifier:   Le, Trung Thanh and Karim, Abed Meraim and Adel, Hafiane and Nguyen, Linh Trung   Sparse Subspace Tracking in High Dimensions.  In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 7-13 May, 2022, Singapore.    (In Press)