DensePILAE: a feature reuse pseudoinverse learning algorithm for deep stacked autoencoder
Autoencoder has been widely used as a feature learning technique. In many works of autoencoder, the features of the original input are usually extracted layer by layer using multi-layer nonlinear mapping, and only the features of the last layer are used for classification or regression. Therefore, t...
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| Vydané v: | Complex & intelligent systems Ročník 8; číslo 3; s. 2039 - 2049 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Springer International Publishing
01.06.2022
Springer Nature B.V |
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| ISSN: | 2199-4536, 2198-6053 |
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| Abstract | Autoencoder has been widely used as a feature learning technique. In many works of autoencoder, the features of the original input are usually extracted layer by layer using multi-layer nonlinear mapping, and only the features of the last layer are used for classification or regression. Therefore, the features of the previous layer aren’t used explicitly. The loss of information and waste of computation is obvious. In addition, faster training and reasoning speed is generally required in the Internet of Things applications. But the stacked autoencoders model is usually trained by the BP algorithm, which has the problem of slow convergence. To solve the above two problems, the paper proposes a dense connection pseudoinverse learning autoencoder (DensePILAE) from reuse perspective. Pseudoinverse learning autoencoder (PILAE) can extract features in the form of analytic solution, without multiple iterations. Therefore, the time cost can be greatly reduced. At the same time, the features of all the previous layers in stacked PILAE are combined as the input of next layer. In this way, the information of all the previous layers not only has no loss, but also can be strengthened and refined, so that better features could be learned. The experimental results in 8 data sets of different domains show that the proposed DensePILAE is effective. |
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| AbstractList | Autoencoder has been widely used as a feature learning technique. In many works of autoencoder, the features of the original input are usually extracted layer by layer using multi-layer nonlinear mapping, and only the features of the last layer are used for classification or regression. Therefore, the features of the previous layer aren’t used explicitly. The loss of information and waste of computation is obvious. In addition, faster training and reasoning speed is generally required in the Internet of Things applications. But the stacked autoencoders model is usually trained by the BP algorithm, which has the problem of slow convergence. To solve the above two problems, the paper proposes a dense connection pseudoinverse learning autoencoder (DensePILAE) from reuse perspective. Pseudoinverse learning autoencoder (PILAE) can extract features in the form of analytic solution, without multiple iterations. Therefore, the time cost can be greatly reduced. At the same time, the features of all the previous layers in stacked PILAE are combined as the input of next layer. In this way, the information of all the previous layers not only has no loss, but also can be strengthened and refined, so that better features could be learned. The experimental results in 8 data sets of different domains show that the proposed DensePILAE is effective. |
| Author | Guo, Ping Li, Yanjun Wang, Jue |
| Author_xml | – sequence: 1 givenname: Jue surname: Wang fullname: Wang, Jue organization: School of Computer Science and Technology, Beijing Institute of Technology, School of Space Information, Space Engineering University – sequence: 2 givenname: Ping surname: Guo fullname: Guo, Ping email: pguo@bnu.edu.cn organization: School of System Science, Beijing Normal University – sequence: 3 givenname: Yanjun surname: Li fullname: Li, Yanjun organization: School of Information, Shanxi University of Finance and Economics |
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| Cites_doi | 10.1016/j.neunet.2019.01.007 10.1109/MCOM.2018.1700298 10.1109/TSC.2017.2662008 10.1007/s12559-021-09853-6 10.1109/TPAMI.2013.50 10.1109/JIOT.2018.2853663 10.1109/TITS.2020.2980555 10.1093/mnras/stw2894 10.1016/j.neucom.2015.09.116 10.1126/science.1127647 10.1109/2.144401 10.1016/S0925-2312(03)00385-0 10.1109/CVPR.2018.00255 10.1007/s40747-021-00319-8 10.1109/CVPR.2016.90 10.1007/s40747-021-00284-2 10.1109/SmartCity.2015.63 10.1109/SMC.2017.8122732 10.1109/ICASSP.2012.6288333 10.1145/1390156.1390294 10.1109/CVPR.2017.76 10.1016/j.neucom.2019.03.024 10.1145/1961189.1961199 10.1109/IJCNN.2018.8489703 10.1109/ICCV.2019.00179 10.1007/978-3-319-92537-0_12 10.1109/CVPR.2017.243 10.1155/2019/9242598 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2021 The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| DOI | 10.1007/s40747-021-00516-5 |
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| SubjectTerms | Algorithms Complexity Computational Intelligence Cost analysis Data Structures and Information Theory Deep learning Engineering Exact solutions Feature extraction Intelligent systems Internet of Things Machine learning Multilayers Neural networks Original Article Teaching methods |
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| Title | DensePILAE: a feature reuse pseudoinverse learning algorithm for deep stacked autoencoder |
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