Kernel-risk-sensitive conjugate gradient algorithm with Student's-t distribution based random fourier features

Kernel-risk-sensitive conjugate gradient algorithm with Student's-t distribution based random fourier features

Kernel-risk-sensitive loss (KRSL) achieves an efficient performance surface, which has been applied in the kernel adaptive filters (KAFs) successfully. However, the KRSL based KAFs use the stochastic gradient descent (SGD) method in the optimization, which usually suffer from inadequate accuracy wit...

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Bibliographic Details
Main Authors: Bi, D. (Author), Li, X. (Author), Li, Z. (Author), Tang, S. (Author), Tang, Y. (Author), Xie, X. (Author), Xie, Y. (Author)
Format: Article
Language:English
Published: John Wiley and Sons Inc 2023
Subjects:
Adaptive filtering
adaptive filters
Adaptive filters
adaptive signal processing
Adaptive signal processing
Conjugate gradient algorithms
Conjugate gradient method
Fourier features
Fourier transforms
Gaussian noise (electronic)
Kernel adaptive filters
Optimisations
Optimization
Performance
Slow convergences
Stochastic gradient descent method
Stochastic systems
Students
Student's t distribution
Student-t distribution
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