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Homogeneous ensemble extreme learning machine autoencoder with mutual representation learning and manifold regularization for medical datasets

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Abstract

As a single learner, extreme learning machine autoencoder (ELM-AE) and generalized extreme learning machine autoencoder (GELM-AE) have limited ability to learn high-dimensional complex data features because high-dimensional data contains more complex and rich discriminative information. GELM-AE only pays attention to the internal relationship of each data subset for dimensionality reduction, ignoring the relationship between different subsets. This paper proposes the homogeneous ensemble extreme learning machine autoencoder (HeELM-AE) to extract high-dimensional complex data diversity features. This method combines the ideas of ensemble feature learning and mutual representation matrix learning. Multiple data subsets are constructed from the original high-dimensional complex dataset with feature learning methods. Generalized extreme learning machine autoencoder(GELM-AE) is used as a base dimension reducer to learn rich discriminative information from highly redundant features. Mutual representation learning methods can characterize correlations between different data subsets and the local manifold structure inherent in different data subsets is maintained through manifold regularization at the same time. Extensive comparative experiments on medical datasets show that compared with other ensemble feature learning models, HeELM-AE is an efficient and accurate model. Finally, visual analysis is used to explain the working mechanism of each stage of HeELM-AE and explore feature learning model interpretability.

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Notes

  1. https://medmnist.github.io/

  2. http://www.cs.binghamton.edu/lyu/KDD08/data/

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Acknowledgements

This research was supported by National Natural Science Foundation of China (Grant No.11571074) and Natural Science Foundation of Fujian Province, China (Grant No. 2022J01102).

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Authors and Affiliations

  1. College of Mathematics and Statistics, Fuzhou University, Fuzhou, 350116, China

    Wenjian Chen, Xiaoyun Chen & Yanming Lin

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  1. Wenjian Chen
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  2. Xiaoyun Chen
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  3. Yanming Lin
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Correspondence to Xiaoyun Chen.

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Chen, W., Chen, X. & Lin, Y. Homogeneous ensemble extreme learning machine autoencoder with mutual representation learning and manifold regularization for medical datasets. Appl Intell 53, 15476–15495 (2023). https://doi.org/10.1007/s10489-022-04284-8

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