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Sparse Subspace Tracking in High Dimensions

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)

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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.

Item Type: Conference or Workshop Item (Paper)
Subjects: Electronics and Communications
ISI/Scopus indexed conference
Divisions: Advanced Insitute of Engineering and Technology (AVITECH)
Depositing User: Lê Trung Thành
Date Deposited: 21 Mar 2022 00:30
Last Modified: 21 Mar 2022 00:30

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