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- W2765330449 abstract "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." @default.
- W2765330449 created "2017-11-10" @default.
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- W2765330449 date "2017-07-01" @default.
- W2765330449 modified "2023-10-17" @default.
- W2765330449 title "High-dimensional Online Adaptive Filtering * *This work is supported by Swedish Research Council under contract 621- 2007-6364. S. Yasini (corresponding author) and K. Pelckmans are with the Division of Systems and Control, Department of Information Technology, Uppsala University, Box 337, SE-751 05 Uppsala, Sweden." @default.
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- W2765330449 doi "https://doi.org/10.1016/j.ifacol.2017.08.1851" @default.
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