: KERNEL RISK-SENSITIVE LOSS: DEFINITION, PROPERTIES AND APPLICATION TO ROBUST ADAPTIVE FILTERING. 2889. TABLE I. SIMILARITY MEASURES IN KERNEL SPACE AND THEIR ...

Aug 1, 2016 · In this work, we propose a new similarity measure in kernel space, called the kernel risk-sensitive loss (KRSL), and provide some important ...

Abstract—Nonlinear similarity measures defined in kernel space, such as correntropy, can extract higher-order statistics of.

Risk-sensitive loss in kernel space for robust adaptive filtering · Generalized Correntropy for Robust Adaptive Filtering · Kernel adaptive filtering with maximum ...

In this work, we propose a new similarity measure in kernel space, called the kernel risk-sensitive loss (KRSL), and provide some important properties. We apply ...

The KRS cost is insensitive to large outliers and can be applied in robust adaptive filtering. Compared with C-Loss, the KRS can achieve faster convergence ...

Abstract—Recently, a robust cost function called C-Loss was proposed for signal processing and machine learning, which is essentially the mean square error ...

To further improve the performance of the kernel based adaptive filtering algorithms, we first define the mixture kernel risk-sensitive loss (MKRSL) and study ...

Jun 13, 2019 · In this paper, the kernel risk-sensitive mean p-power error (KRP) criterion is proposed by constructing mean p-power error (MPE) into kernel ...

Jul 1, 2020 · The proposed MCKRSL with variable parameters (MCKRSL-VP) algorithm updates the risk-sensitive parameter and kernel width by making the ...