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Extreme Learning Machine Combining Hidden-Layer Feature Weighting and Batch Training for Classification

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Abstract

Based on the network structure and training methods of extreme learning machines, extreme learning machine combining hidden-layer feature weighting and batch training (ELM-WB) is proposed to make full use of representation-level features for object images and human action videos classification. To solve the problem of insufficient fusion of multiple representation-level features in most classification methods, a double hidden layer structure in which the input layer and the second hidden layer are directly connected is designed. A loop training method of weighting coefficients and output weights is proposed based on the advantages of this structure. The proposed network structure and training method are combined to construct an extreme learning machine combining hidden-layer feature weighting (ELM-W), which can effectively fuse representation-level features to enhance the classification ability of representation-level features. On this basis, the principle of online sequential ELM (OS-ELM) is introduced to update the loop training formula of the two weights to reduce the memory consumption during the operation of the overall algorithm. ELM-WB is proposed by combining the loop training formula of two weight matrices with batch training. In order to test the feasibility of ELM-WB, experiments are conducted on Caltech 101, MSRC, UCF11 and UCF101 databases. Experimental results prove that the proposed ELM-WB can improve classification accuracy by fusing representation-level features. At the same time, ELM-WB can be used to perform classification tasks on databases of any size in a general-purpose computer without specific hardware.

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Data Availability

The Caltech 101 [34], MSRC [35], UCF11 [36] and UCF101 [27] databases that are used to support the findings of this study could be found freely here: https://data.caltech.edu/records/mzrjq-6wc02https://www.microsoft.com/en-us/download/details.aspx?id=52644https://www.crcv.ucf.edu/data/UCF_YouTube_Action.php and https://www.crcv.ucf.edu/data/UCF101.php, respectively.

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Acknowledgements

This work is supported by High-Level Talent Scientific Research Start-up Foundation of Henan Institute of Technology of China (KQ2109)

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Authors and Affiliations

  1. School of Electrical Engineering and Automation, Henan Institute of Technology, Xinxiang, 453003, China

    Chao Wu & Yakun Gao

  2. College of Computer Science and Technology, Henan Institute of Technology, Xinxiang, 453003, China

    Yu Sang

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CW wrote the main manuscript text. All authors reviewed the manuscript.

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Correspondence to Chao Wu.

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Wu, C., Sang, Y. & Gao, Y. Extreme Learning Machine Combining Hidden-Layer Feature Weighting and Batch Training for Classification. Neural Process Lett 55, 10951–10973 (2023). https://doi.org/10.1007/s11063-023-11358-2

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