Abstract
Due to the poor robustness and high complexity of the concentrated dictionary learning (CDL) algorithm, this paper addresses these issues using denoising deep extreme learning machine based on autoencoder (DDELM-AE) and approximate k singular value decomposition (AK-SVD). Different from the CDL algorithm, on input, DDELM-AE is added for enhancing denoising ability and AK-SVD replaces K-SVD for improving running speed. Additionally, experimental results show that the improved algorithm is more efficient than the original CDL algorithm in terms of running time, denoising ability, and stability.
Junwei Mao, M.S., Chongqing University of Posts and Telecommunications. His current main research interest includes: signal processing and deep learning.
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Acknowledgments
This work is supported by Natural Science Foundation of China (Grant No. 61702066), Scientific and Technological Research Program of Chongqing Municipal Education Commission (Grant No. KJ1704080), and Chongqing Research Program of Basic Research and Frontier Technology (Grant No. cstc2017jcyjAX0256).
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Yu, X., Mao, J., Gan, C., Zhang, Z. (2020). Improved Performance of CDL Algorithm Using DDELM-AE and AK-SVD. In: Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2018. Lecture Notes in Electrical Engineering, vol 516. Springer, Singapore. https://doi.org/10.1007/978-981-13-6504-1_105
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DOI: https://doi.org/10.1007/978-981-13-6504-1_105
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