A fast kernel extreme learning machine based on conjugate gradient

ABSTRACT

Kernel extreme learning machine (KELM) introduces kernel leaning into extreme learning machine (ELM) in order to improve the generalization ability and stability. But the Penalty parameter in KELM is randomly set and it has a strong impact on the performance of KELM. A fast KELM combining the conjugate gradient method (CG-KELM) is presented in this paper. The CG-KELM computes the output weights of the neural network by the conjugate gradient iteration method. There is no penalty parameter to be set in CG-KELM. Therefore, the CG-KELM has good generalization ability and fast learning speed. The simulations in image restoration show that CG-KELM outperforms KELM. The CG-KELM provides a balanced method between KELM and ELM.

KEYWORDS:

• Kernel extreme learning machine
• conjugate gradient method
• generalization ability
• image restoration

Additional information

Funding

This work is supported by the National Natural Science Foundation of China (Grant No. 61402227, 61502407,61772178, 61873221,61672447), the Project in Hunan province department of education (Grant No. 16C1546), the project of Xiangtan University (Grant No. 11kz/kz08055) and the Natural Science Foundation of Hunan Province (Grant no. 2019JJ50618, 2018JJ4058, 2017JJ5036).