Abstract
In last decade, a large number of research has focused on the sparse representation for signal. As a dictionary learning algorithm, K-SVD, is introduced to efficiently learn an redundant dictionary from a set of training signals. In the mean time, there is an interesting technique named extreme learning machines (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 denoising deep extreme learning machines based on autoencoder (DDELM-AE) for sparse representation. It makes the conventional K-SVD algorithm perform better. Finally, we show the experimental rusults on our optimized method and the typical K-SVD algorithm.
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Acknowledgments
This work was supported in part by the National Key Project for Basic Research of China under Grant 2013CB329403; in part by the National Natural Science Foundation of China under Grant 61210013; and in part by the Tsinghua University Initiative Scientific Research Program under Grant 20131089295.
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Cheng, X., Liu, H., Xu, X., Sun, F. (2016). Denoising Deep Extreme Learning Machines for Sparse Representation. In: Cao, J., Mao, K., Wu, J., Lendasse, A. (eds) Proceedings of ELM-2015 Volume 2. Proceedings in Adaptation, Learning and Optimization, vol 7. Springer, Cham. https://doi.org/10.1007/978-3-319-28373-9_20
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DOI: https://doi.org/10.1007/978-3-319-28373-9_20
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