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SPARSE SUBSPACE TRACKING IN HIGH DIMENSIONS

Citation Author(s):
Le Trung Thanh, Karim Abed-Meraim, Adel Hafiane, Nguyen Linh Trung
Submitted by:
Le Thanh
Last updated:
4 May 2022 - 4:43pm
Document Type:
Presentation Slides
Document Year:
2022
Event:
Presenters:
Le Trung Thanh
Paper Code:
SPTM-20.1

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.

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