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A unified distributed ELM framework with supervised, semi-supervised and unsupervised big data learning

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Abstract

Extreme learning machine (ELM) as well as its variants have been widely used in many fields for its good generalization performance and fast learning speed. Though distributed ELM can sufficiently process large-scale labeled training data, the current technology is not able to process partial labeled or unlabeled training data. Therefore, we propose a new unified distributed ELM with supervised, semi-supervised and unsupervised learning based on MapReduce framework, called U-DELM. The U-DELM method can be used to overcome the existing distributed ELM framework’s lack of ability to process partially labeled and unlabeled training data. We first compare the computation formulas of supervised, semi-supervised and unsupervised learning methods and found that the majority of expensive computations are decomposable. Next, MapReduce framework based U-DELM is proposed, which extracts three different matrices continued multiplications from the three computational formulas introduced above. After that, we transform the cumulative sums respectively to make them suitable for MapReduce. Then, the combination of the three computational formulas are used to solve the output weight in three different learning methods. Finally, by using benchmark and synthetic datasets, we are able to test and verify the efficiency and effectiveness of U-DELM on learning massive data. Results prove that U-DELM can achieve unified distribution on supervised, semi-supervised and unsupervised learning.

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Acknowledgements

This research was partially supported by the following foundations: the National Natural Science Foundation of China under Grant Nos. 61472069, 61402089, and U1401256. The Fundamental Research Funds for the Central Universities under Grant Nos. N161602003, N171607010, N161904001, and N160601001. The Natural Science Foundation of Liaoning Province under Grant No. 2015020553.

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Authors and Affiliations

  1. Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, China

    Zhiqiong Wang, Luxuan Qu & Xiaosong Gao

  2. School of Computer Science and Engineering, Key Laboratory of Big Data Management and Analytics (Liaoning Province), Northeastern University, Shenyang, China

    Junchang Xin & Hongxu Yang

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  1. Zhiqiong Wang
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  2. Luxuan Qu
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  3. Junchang Xin
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  4. Hongxu Yang
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  5. Xiaosong Gao
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Correspondence to Junchang Xin.

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Wang, Z., Qu, L., Xin, J. et al. A unified distributed ELM framework with supervised, semi-supervised and unsupervised big data learning. Memetic Comp. 11, 305–315 (2019). https://doi.org/10.1007/s12293-018-0271-8

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