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Kernel Recursive Maximum Versoria Criterion Algorithm Using Random Fourier Features
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DOI：10.1109/TCSII.2021.3056729.png)
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Abstract
En 中文
Reproducing Hilbert space (RKHS)-based adaptive algorithms have attracted increased attention in machine learning and nonlinear signal processing with applications in visible light communications, radar, radio frequency communications and others. However, performance of RKHS-based algorithms is highly sensitive to a suitable learning criterion. In this regard, the Versoria criterion-based adaptive filtering has gained interest in recent works due to its superior convergence characteristics as compared to the popular criterion such as minimum mean square error, and maximum correntropy criterion. Therefore, in this brief, a novel random Fourier feature (RFF)-based kernel recursive maximum Versoria criterion (KRMVC) algorithm is proposed. Convergence analysis is presented next for the proposed RFF-KRMVC algorithm as guarantees of the promised performance benefits. Lastly, the analytical results are validated by corresponding computer-simulations over practical application-scenarios considered in this brief.
Keywords:
Signal processing algorithms
Kernel
Convergence
Computational complexity
Steady-state
Prediction algorithms
Visible light communication
RKHS
minimum mean square error
correntropy
RFF
Versoria criterion
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