Abstract
In this paper, a novel sparse kernel recursive least squares algorithm, namely the Projected Kernel Recursive Least Squares (PKRLS) algorithm, is proposed. In PKRLS, a simple online vector projection (VP) method is used to represent the similarity between the current input and the dictionary in a feature space. The use of projection method applies sufficiently the information contained in data to update our solution. Compared with the quantized kernel recursive least squares (QKRLS) algorithm, which is a kind of kernel adaptive filter using vector quantization (VQ) in input space, simulation results validate that PKRLS can achieve a comparable filtering performance in terms of sparse network sizes and testing mean square error.
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Acknowledgments
The work was supported in part by the National Natural Science Foundation of China (Grant nos. 61374117,61004048, 61174137, and 61104064), the National Science Foundation of Jiang Su Province (Grant no. BK2010493), the grant from the Science and Technology Department of Sichuan Province (Grant no. 2014GZ0156), and the grant from the China Postdoctoral Science Foundation funded project (Grant no. 2012M510135).
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Zhao, J., Zhang, H. (2017). Projected Kernel Recursive Least Squares Algorithm. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10634. Springer, Cham. https://doi.org/10.1007/978-3-319-70087-8_38
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DOI: https://doi.org/10.1007/978-3-319-70087-8_38
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