Correntropy induced loss based sparse robust graph regularized extreme learning machine for cancer classification
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METHODOLOGY ARTICLE
Open Access
Correntropy induced loss based sparse robust graph regularized extreme learning machine for cancer classification Liang‑Rui Ren1, Ying‑Lian Gao2, Jin‑Xing Liu1* , Junliang Shang1 and Chun‑Hou Zheng1,3 *Correspondence: [email protected] 1 School of Computer Science, Qufu Normal University, Rizhao 276826, China Full list of author information is available at the end of the article
Abstract Background: As a machine learning method with high performance and excellent generalization ability, extreme learning machine (ELM) is gaining popularity in various studies. Various ELM-based methods for different fields have been proposed. However, the robustness to noise and outliers is always the main problem affecting the perfor‑ mance of ELM. Results: In this paper, an integrated method named correntropy induced loss based sparse robust graph regularized extreme learning machine (CSRGELM) is proposed. The introduction of correntropy induced loss improves the robustness of ELM and weak‑ ens the negative effects of noise and outliers. By using the L2,1-norm to constrain the output weight matrix, we tend to obtain a sparse output weight matrix to construct a simpler single hidden layer feedforward neural network model. By introducing the graph regularization to preserve the local structural information of the data, the clas‑ sification performance of the new method is further improved. Besides, we design an iterative optimization method based on the idea of half quadratic optimization to solve the non-convex problem of CSRGELM. Conclusions: The classification results on the benchmark dataset show that CSRGELM can obtain better classification results compared with other methods. More impor‑ tantly, we also apply the new method to the classification problems of cancer samples and get a good classification effect. Keywords: Extreme learning machine, Correntropy induced loss, Supervised learning, Bioinformatics
Background Universal approximation capability plays a crucial role in settling regression and classification problems. Because of this ability, the single hidden layer feedforward neural network has always been the focus and hotspot of researches [1]. As a method to train the SLFNs [2], extreme learning machine (ELM) [3–8] has attracted the attention of researchers in recent decades [9]. Different from traditional neural network models, such as the backpropagation (BP) algorithm [10, 11], the training process of ELM is implemented in one step rather than iteratively [12]. In the original ELM, the first step © The Author(s) 2020. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons lice
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