2026-09-20T13:02:25Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/49503 2023-07-11 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

Huang, Thomas S. Xu, Xingqian 2014-05-30T16:47:30Z 2014-05-30T16:47:30Z 2014-05 2014-05-30T16:47:30Z 2014-05 Robust Principal Component Analysis (RPCA) methods have become very popular in the past ten years. Many publications show that RPCA provides good results for background subtraction problems. In this thesis, we further the exploration to online versions of RPCA algorithms. The proposed Online Robust Principal Component Analysis (ORPCA) is used to process big data in a more efficient way. We also test the algorithm performances on the Toyota car data set provided by the Toyota Motor Corporation. Meanwhile, a comprehensive comparison of the algorithm performance is also shown based on testing results and running efficiency. Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2014-04-17T19:33:28Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Xu_Xingqian.pdf: 945139 bytes, checksum: 193af8a0976623365af2b546c0d51825 (MD5) Made available in DSpace on 2014-05-30T16:47:30Z (GMT). No. of bitstreams: 2 Xingqian_Xu.pdf: 945139 bytes, checksum: 193af8a0976623365af2b546c0d51825 (MD5) license.txt: 4057 bytes, checksum: d638499c5a3e9cba38227e626aa0d202 (MD5) http://hdl.handle.net/2142/49503 en Copyright 2014 Xingqian Xu Online RPCA Robust Principal Component Analysis (RPCA) Online robust principal component analysis for background subtraction: a system evaluation on Toyota car data text Electrical & Computer Eng 1933 Electrical & Computer Engr 1200 University of Illinois at Urbana-Champaign Thesis M.S. MS:Electr & Computer Eng-UIUC 10KS1200MS