Correntropy induced loss based sparse robust graph regularized extreme learning machine for cancer classification
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 al...
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doaj-acb457e7931d429fa7a9d0685902a2112020-11-25T03:53:47ZengBMCBMC Bioinformatics1471-21052020-10-0121112210.1186/s12859-020-03790-1Correntropy induced loss based sparse robust graph regularized extreme learning machine for cancer classificationLiang-Rui Ren0Ying-Lian Gao1Jin-Xing Liu2Junliang Shang3Chun-Hou Zheng4School of Computer Science, Qufu Normal UniversityQufu Normal University Library, Qufu Normal UniversitySchool of Computer Science, Qufu Normal UniversitySchool of Computer Science, Qufu Normal UniversitySchool of Computer Science, Qufu Normal UniversityAbstract 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 performance 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 weakens the negative effects of noise and outliers. By using the L 2,1-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 classification 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 importantly, we also apply the new method to the classification problems of cancer samples and get a good classification effect.http://link.springer.com/article/10.1186/s12859-020-03790-1Extreme learning machineCorrentropy induced lossSupervised learningBioinformatics |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Liang-Rui Ren Ying-Lian Gao Jin-Xing Liu Junliang Shang Chun-Hou Zheng |
spellingShingle |
Liang-Rui Ren Ying-Lian Gao Jin-Xing Liu Junliang Shang Chun-Hou Zheng Correntropy induced loss based sparse robust graph regularized extreme learning machine for cancer classification BMC Bioinformatics Extreme learning machine Correntropy induced loss Supervised learning Bioinformatics |
author_facet |
Liang-Rui Ren Ying-Lian Gao Jin-Xing Liu Junliang Shang Chun-Hou Zheng |
author_sort |
Liang-Rui Ren |
title |
Correntropy induced loss based sparse robust graph regularized extreme learning machine for cancer classification |
title_short |
Correntropy induced loss based sparse robust graph regularized extreme learning machine for cancer classification |
title_full |
Correntropy induced loss based sparse robust graph regularized extreme learning machine for cancer classification |
title_fullStr |
Correntropy induced loss based sparse robust graph regularized extreme learning machine for cancer classification |
title_full_unstemmed |
Correntropy induced loss based sparse robust graph regularized extreme learning machine for cancer classification |
title_sort |
correntropy induced loss based sparse robust graph regularized extreme learning machine for cancer classification |
publisher |
BMC |
series |
BMC Bioinformatics |
issn |
1471-2105 |
publishDate |
2020-10-01 |
description |
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 performance 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 weakens the negative effects of noise and outliers. By using the L 2,1-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 classification 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 importantly, we also apply the new method to the classification problems of cancer samples and get a good classification effect. |
topic |
Extreme learning machine Correntropy induced loss Supervised learning Bioinformatics |
url |
http://link.springer.com/article/10.1186/s12859-020-03790-1 |
work_keys_str_mv |
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1724476575002394624 |