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- W2766675239 endingPage "179" @default.
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- W2766675239 abstract "In this paper, we study the Nystr{o}m type subsampling for large scale kernel methods to reduce the computational complexities of big data. We discuss the multi-penalty regularization scheme based on Nystr{o}m type subsampling which is motivated from well-studied manifold regularization schemes. We develop a theoretical analysis of multi-penalty least-square regularization scheme under the general source condition in vector-valued function setting, therefore the results can also be applied to multi-task learning problems. We achieve the optimal minimax convergence rates of multi-penalty regularization using the concept of effective dimension for the appropriate subsampling size. We discuss an aggregation approach based on linear function strategy to combine various Nystr{o}m approximants. Finally, we demonstrate the performance of multi-penalty regularization based on Nystr{o}m type subsampling on Caltech-101 data set for multi-class image classification and NSL-KDD benchmark data set for intrusion detection problem." @default.
- W2766675239 created "2017-11-10" @default.
- W2766675239 creator A5040022967 @default.
- W2766675239 creator A5074889377 @default.
- W2766675239 date "2020-07-01" @default.
- W2766675239 modified "2023-10-16" @default.
- W2766675239 title "Manifold regularization based on Nyström type subsampling" @default.
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- W2766675239 doi "https://doi.org/10.1016/j.acha.2018.12.002" @default.