Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Kernel Risk-Sensitive Loss: Definition, Properties and Application to
  Robust Adaptive Filtering   Nonlinear similarity measures defined in kernel space, such as correntropy,
can extract higher-order statistics of data and offer potentially significant
performance improvement over their linear counterparts especially in
non-Gaussian signal processing and machine learning. 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 KRSL to adaptive
filtering and investigate the robustness, and then develop the MKRSL algorithm
and analyze the mean square convergence performance. Compared with correntropy,
the KRSL can offer a more efficient performance surface, thereby enabling a
gradient based method to achieve faster convergence speed and higher accuracy
while still maintaining the robustness to outliers. Theoretical analysis
results and superior performance of the new algorithm are confirmed by
simulation.

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The categories of this paper are: acc-phys
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The categories of this paper are: hep-lat
6
The categories of this paper are: astro-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: supr-con
5
The categories of this paper are: gr-qc
4
The categories of this paper are: cond-mat
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The categories of this paper are: q-bio
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The categories of this paper are: math
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The categories of this paper are: eess
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The categories of this paper are: econ
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The categories of this paper are: bayes-an
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The categories of this paper are: funct-an
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The categories of this paper are: nucl-th
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The categories of this paper are: solv-int