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High-dimensional online adaptive filtering
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
2017 (English)Conference paper, Published paper (Refereed)
Abstract [en]

While recent advances in online learning arising from the universal prediction perspective have led to algorithms with prediction performance guarantee, these techniques are not able to cope with high-dimensional data. This paper analyses a random projection gradient descent (RP-GD) algorithm which addresses this challenge and yields low theoretical regret bound and computationally efficient algorithm. It is shown that the performance of the algorithm converges to the performance of the best offline, computationally complex, high dimensional algorithm in hindsight.

Place, publisher, year, edition, pages
Elsevier, 2017. Vol. 50, p. 14106-14111
Series
IFAC-PapersOnLine, ISSN 2405-8963 ; 50:1
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:uu:diva-321311DOI: 10.1016/j.ifacol.2017.08.1851ISI: 000423965200340OAI: oai:DiVA.org:uu-321311DiVA, id: diva2:1092447
Conference
20th World Congress of the International-Federation-of-Automatic-Control (IFAC), Toulouse, France, July 9–14, 2017
Funder
Swedish Research Council, 621- 2007-6364Available from: 2017-10-18 Created: 2017-05-02 Last updated: 2019-02-28Bibliographically approved

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Yasini, SholehPelckmans, Kristiaan

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