eprintid: 4712
rev_number: 8
eprint_status: archive
userid: 381
dir: disk0/00/00/47/12
datestamp: 2022-03-21 00:30:45
lastmod: 2022-03-21 00:30:45
status_changed: 2022-03-21 00:30:45
type: conference_item
metadata_visibility: show
creators_name: Le, Trung Thanh
creators_name: Karim, Abed Meraim
creators_name: Adel, Hafiane
creators_name: Nguyen, Linh Trung
creators_id: letrungthanhtbt@gmail.com
creators_id: karim.abed-meraim@univ-orleans.fr
creators_id: adel.hafiane@insa-cvl.fr
creators_id: linhtrung@vnu.edu.vn
title: Sparse Subspace Tracking in High Dimensions
ispublished: inpress
subjects: ECE
subjects: isi_scopus_conf
divisions: avitech
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.
date_type: completed
full_text_status: public
pres_type: paper
event_title: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
event_location: Singapore
event_dates: 7-13 May, 2022
event_type: conference
refereed: TRUE
citation:   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)  
document_url: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/4712/1/ICASSP2022_published.pdf