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• W2197043100 abstract "The paper develops recursive least square algorithms for nonlinear filtering of multivariate or functional data streams. The framework relies on kernel Hilbert spaces of operators. The results generalize to this framework the kernel recursive least squares developed in the scalar case. We particularly propose two possible extensions of the notion of approximate linear dependence of the regressors, which in the context of the paper, are operators. The development of the algorithms are done in infinite-dimensional spaces using matrices of operators. The algorithms are easily written in finite-dimensional settings using block matrices, and are illustrated in this context for the prediction of a bivariate time series." @default.
• W2197043100 created "2016-06-24" @default.
• W2197043100 creator A5038576947 @default.
• W2197043100 creator A5079506428 @default.
• W2197043100 date "2015-08-01" @default.
• W2197043100 modified "2023-09-25" @default.
• W2197043100 title "Operator-valued kernel recursive least squares algorithm" @default.
• W2197043100 cites W1153891179 @default.
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• W2197043100 doi "https://doi.org/10.1109/eusipco.2015.7362810" @default.
• W2197043100 hasPublicationYear "2015" @default.
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