no code implementations • 1 Apr 2024 • Yuanhao Li, Badong Chen, Natsue Yoshimura, Yasuharu Koike, Okito Yamashita
Sparse Bayesian learning has promoted many effective frameworks for brain activity decoding, especially for the reconstruction of muscle activity.
no code implementations • 31 Jan 2023 • Yuanhao Li, Badong Chen, Okito Yamashita, Natsue Yoshimura, Yasuharu Koike
In the present study, regarding the maximum correntropy criterion (MCC) based robust regression algorithm, we investigate to integrate the MCC method with the automatic relevance determination (ARD) technique in a Bayesian framework, so that MCC-based robust regression could be implemented with adaptive sparseness.
no code implementations • 20 Jul 2022 • Yuanhao Li, Badong Chen, Yuxi Shi, Natsue Yoshimura, Yasuharu Koike
To this end, we introduce the correntropy learning framework into the automatic relevance determination based sparse classification model, proposing a new correntropy-based robust sparse logistic regression algorithm.
no code implementations • 23 Jun 2021 • Yuanhao Li, Badong Chen, Gang Wang, Natsue Yoshimura, Yasuharu Koike
The aim of this study is to propose a new robust implementation for PLSR.
no code implementations • 6 Sep 2019 • Yuanhao Li, Badong Chen, Natsue Yoshimura, Yasuharu Koike
The minimum error entropy (MEE) criterion has been verified as a powerful approach for non-Gaussian signal processing and robust machine learning.
no code implementations • 23 Nov 2017 • Wentao Ma, Dongqiao Zheng, Yuanhao Li, ZhiYu Zhang, Badong Chen
This paper proposed a bias-compensated normalized maximum correntropy criterion (BCNMCC) algorithm charactered by its low steady-state misalignment for system identification with noisy input in an impulsive output noise environment.