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An initial study on the rank of input matrix for extreme learning machine

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Abstract

It is noted that the rank of input data matrix has a critical impact on the performance of a trained classifier model. This paper presents a study on the rank of input data matrix based on a classification model of extreme learning machine which is a single hidden layer feed-forward neural network with non-iterative training. The changing tendency of model accuracy with the increase of input data matrix rank is experimentally investigated and the relationship between the input matrix rank and classification problem complexity is addressed. The analysis and experiments show some meaningful results.

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Acknowledgements

The first author, Miss Xingmin Zhao, would like to thank her supervisor Professor Xizhao Wang for his guidance and suggestion to improve this paper. This study was supported by Basic Research Project of Knowledge Innovation Program in Shenzhen (JCYJ20150324140036825), and National Natural Science Foundations of China (71371063, 61672358).

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Authors and Affiliations

  1. Big Data Institute, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

    Xingmin Zhao, Weipeng Cao, Hongyu Zhu, Zhong Ming & Rana Aamir Raza Ashfaq

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  1. Xingmin Zhao
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  2. Weipeng Cao
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  3. Hongyu Zhu
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  4. Zhong Ming
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  5. Rana Aamir Raza Ashfaq
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Correspondence to Xingmin Zhao.

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Zhao, X., Cao, W., Zhu, H. et al. An initial study on the rank of input matrix for extreme learning machine. Int. J. Mach. Learn. & Cyber. 9, 867–879 (2018). https://doi.org/10.1007/s13042-016-0615-y

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  • DOI: https://doi.org/10.1007/s13042-016-0615-y

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