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Denoising deep extreme learning machine for sparse representation

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Abstract

In recent years, a great deal of research has focused on the sparse representation for signal. Particularly, a dictionary learning algorithm, K-SVD, is introduced to efficiently learn an redundant dictionary from a set of training signals. Indeed, much progress has been made in different aspects. In addition, there is an interesting technique named extreme learning machine (ELM), which is an single-layer feed-forward neural networks (SLFNs) with a fast learning speed, good generalization and universal classification capability. In this paper, we propose an optimization method about K-SVD, which is an denoising deep extreme learning machines based on autoencoder (DDELM-AE) for sparse representation. In other words, we gain a new learned representation through the DDELM-AE and as the new “input”, it makes the conventional K-SVD algorithm perform better. To verify the classification performance of the new method, we conduct extensive experiments on real-world data sets. The performance of the deep models (i.e., Stacked Autoencoder) is comparable. The experimental results indicate the fact that our proposed method is very efficient in the sight of speed and accuracy.

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Acknowledgments

This work was supported in part by the National Key Project for Basic Research of China under Grant 2013CB329403, the National Natural Science Foundation of China under Grant 61327809, the National High-Tech Research and Development Plan under Grant 2015AA042306, the Natural Science Foundation of Shanxi Province under Grant 2014011018-4, the Shanxi Scholarship Council of China under Grant 2013-033, and the Shanxi Scholarship Council of China under Grant 2015-045

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Authors and Affiliations

  1. Department of Electronic Information, Taiyuan University of Technology, Shanxi, China

    Xiangyi Cheng & Xinying Xu

  2. State Key Laboratory of Intelligent Technology and Systems, TNLIST, Department of Computer Science and Technology, Tsinghua University, Beijing, People’s Republic of China

    Xiangyi Cheng, Huaping Liu & Fuchun Sun

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  1. Xiangyi Cheng
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  2. Huaping Liu
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  3. Xinying Xu
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Correspondence to Huaping Liu.

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Cheng, X., Liu, H., Xu, X. et al. Denoising deep extreme learning machine for sparse representation. Memetic Comp. 9, 199–212 (2017). https://doi.org/10.1007/s12293-016-0185-2

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