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.
CITATION STYLE
Zhao, J., & Zhang, H. (2017). Projected kernel recursive least squares algorithm. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10634 LNCS, pp. 356–365). Springer Verlag. https://doi.org/10.1007/978-3-319-70087-8_38
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