Online compressed robust PCA

Pan, P; Feng, J; Chen, L; Yang, Y

Online compressed robust PCA

Pan, P Feng, J Chen, L

Yang, Y

Permalink

Publication Type:: Conference Proceeding
Citation:: Proceedings of the International Joint Conference on Neural Networks, 2017, 2017-May pp. 1041 - 1048
Issue Date:: 2017-06-30

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download full textAdobe PDF (518.79 kB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Pan, P	en_US
dc.contributor.author	Feng, J	en_US
dc.contributor.author	Chen, L https://orcid.org/0000-0002-6468-5729	en_US
dc.contributor.author	Yang, Y https://orcid.org/0000-0001-5528-0546	en_US
dc.date.available	2020-05-25T19:08:05Z
dc.date.issued	2017-06-30	en_US
dc.identifier.citation	Proceedings of the International Joint Conference on Neural Networks, 2017, 2017-May pp. 1041 - 1048	en_US
dc.identifier.isbn	9781509061815	en_US
dc.identifier.uri	http://hdl.handle.net/10453/126481
dc.description.abstract	© 2017 IEEE. In this work, we consider the problem of robust principal component analysis (RPCA) for streaming noisy data that has been highly compressed. This problem is prominent when one deals with high-dimensional and large-scale data and data compression is necessary. To solve this problem, we propose an online compressed RPCA algorithm to efficiently recover the low-rank components of raw data. Though data compression incurs severe information loss, we provide deep analysis on the proposed algorithm and prove that the low-rank component can be asymptotically recovered under mild conditions. Compared with other recent works on compressed RPCA, our algorithm reduces the memory cost significantly by processing data in an online fashion and reduces the communication cost by accepting sequential compressed data as input.	en_US
dc.relation.ispartof	Proceedings of the International Joint Conference on Neural Networks	en_US
dc.relation.isbasedon	10.1109/IJCNN.2017.7965967	en_US
dc.title	Online compressed robust PCA	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2017-May	en_US
utslib.for	0804 Data Format	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	2017-May	en_US

Abstract:

© 2017 IEEE. In this work, we consider the problem of robust principal component analysis (RPCA) for streaming noisy data that has been highly compressed. This problem is prominent when one deals with high-dimensional and large-scale data and data compression is necessary. To solve this problem, we propose an online compressed RPCA algorithm to efficiently recover the low-rank components of raw data. Though data compression incurs severe information loss, we provide deep analysis on the proposed algorithm and prove that the low-rank component can be asymptotically recovered under mild conditions. Compared with other recent works on compressed RPCA, our algorithm reduces the memory cost significantly by processing data in an online fashion and reduces the communication cost by accepting sequential compressed data as input.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/126481