A Quantized Kernel Learning Algorithm Using a Minimum Kernel Risk-Sensitive Loss Criterion and Bilateral Gradient Technique

A Quantized Kernel Learning Algorithm Using a Minimum Kernel Risk-Sensitive Loss Criterion and Bilateral Gradient Technique

Recently, inspired by correntropy, kernel risk-sensitive loss (KRSL) has emerged as a novel nonlinear similarity measure defined in kernel space, which achieves a better computing performance. After applying the KRSL to adaptive filtering, the corresponding minimum kernel risk-sensitive loss (MKRSL)...

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Bibliographic Details
Main Authors: Xiong Luo, Jing Deng, Weiping Wang, Jenq-Haur Wang, Wenbing Zhao
Format: Article
Language:English
Published: MDPI AG 2017-07-01
Series:Entropy
Subjects:
kernel methods
correntropy
kernel risk-sensitive loss
online vector quantization
Online Access:https://www.mdpi.com/1099-4300/19/7/365

Internet

https://www.mdpi.com/1099-4300/19/7/365

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