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SPARSE SUBSPACE TRACKING IN HIGH DIMENSIONS

- Citation Author(s):
- 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
- Categories:
- Keywords:
Abstract
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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.
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