Using Wavelet Packet Denoising and a Regularized ELM Algorithm Based on the LOO Approach for Transient Electromagnetic Inversion

Li, Ruiyou; Wang, Chao; Zhang, Huaiqing; Guo, Chunxian; Wu, Zhao

doi:10.1109/tgrs.2022.3151339

Cited by 10 publications

(2 citation statements)

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“…To further improve the generalization performance and the stability, regularization theory is imported into the ELM to minimize the training error and the norm of the output weight matrix β [27][28][29]. The RELM solves for the output weight β in the following RLS problem min…”

Section: Relm Methodsmentioning

confidence: 99%

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A Maximally Split and Adaptive Relaxed Alternating Direction Method of Multipliers for Regularized Extreme Learning Machines

et al. 2023

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One of the significant features of extreme learning machines (ELMs) is their fast convergence. However, in the big data environment, the ELM based on the Moore–Penrose matrix inverse still suffers from excessive calculation loads. Leveraging the decomposability of the alternating direction method of multipliers (ADMM), a convex model-fitting problem can be split into a set of sub-problems which can be executed in parallel. Using a maximally splitting technique and a relaxation technique, the sub-problems can be split into multiple univariate sub-problems. On this basis, we propose an adaptive parameter selection method that automatically tunes the key algorithm parameters during training. To confirm the effectiveness of this algorithm, experiments are conducted on eight classification datasets. We have verified the effectiveness of this algorithm in terms of the number of iterations, computation time, and acceleration ratios. The results show that the method proposed by this paper can greatly improve the speed of data processing while increasing the parallelism.

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Section: Relm Methodsmentioning

confidence: 99%

“…Given the hidden-layer output matrix H, the optimal output weights of the MS-ARADMM are calculated with (27). The output weights of the RB-ADMM and the MS-AADMM are updated by the following:…”

Section: Convergence Analysismentioning

confidence: 99%