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Robust low-rank multiple kernel learning with compound regularization
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DOI：10.1016/j.ejor.2020.12.024.png)
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Kernel learning is widely used in nonlinear models during the implementation of forecasting tasks in analytics. However, existing forecasting models lack robustness and accuracy. Therefore, in this study, a novel supervised forecasting approach based on robust low-rank multiple kernel learning with com-pound regularization is investigated. The proposed method extracts the benefits from robust regression, multiple kernel learning with low-rank approximation, and sparse learning systems. Unlike existing hy-brid forecasting methods, which frequently combine different models in parallel, we embed a Huber or quantile loss function and a compound regularization composed of smoothly clipped absolute deviation and ridge regularizations in a support vector machine with predefined number of kernels. To select the optimal kernels, L 1 penalization with positive constraint is also considered. The proposed model exhibits robustness, forecasting accuracy, and sparsity in the reproducing kernel Hilbert space. For computation, a simple algorithm is designed based on local quadratic approximation to implement the proposed method. Theoretically, the forecasting and estimation error bounds of the proposed estimators are derived under a null consistency assumption. Real data experiments using datasets from various scientific research fields demonstrate the superior performances of the proposed approach compared with other state-of-the-art competitors. (C) 2020 Elsevier B.V. All rights reserved.
Keyword:
Analytics
Robust estimation
Sparse learning
Multiple kernel learning
Compound regularization
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6
论文数:
2.2W
被引数:
6.4W
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