Kernel-Based Multilayer Extreme Learning Machines for Representation Learning
Recently, multilayer extreme learning machine (ML-ELM) was applied to stacked autoencoder (SAE) for representation learning. In contrast to traditional SAE, the training time of ML-ELM is significantly reduced from hours to seconds with high accuracy. However, ML-ELM suffers from several drawbacks:...
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| Veröffentlicht in: | IEEE transaction on neural networks and learning systems Jg. 29; H. 3; S. 757 - 762 |
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| Hauptverfasser: | , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
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United States
IEEE
01.03.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 2162-237X, 2162-2388 |
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| Abstract | Recently, multilayer extreme learning machine (ML-ELM) was applied to stacked autoencoder (SAE) for representation learning. In contrast to traditional SAE, the training time of ML-ELM is significantly reduced from hours to seconds with high accuracy. However, ML-ELM suffers from several drawbacks: 1) manual tuning on the number of hidden nodes in every layer is an uncertain factor to training time and generalization; 2) random projection of input weights and bias in every layer of ML-ELM leads to suboptimal model generalization; 3) the pseudoinverse solution for output weights in every layer incurs relatively large reconstruction error; and 4) the storage and execution time for transformation matrices in representation learning are proportional to the number of hidden layers. Inspired by kernel learning, a kernel version of ML-ELM is developed, namely, multilayer kernel ELM (ML-KELM), whose contributions are: 1) elimination of manual tuning on the number of hidden nodes in every layer; 2) no random projection mechanism so as to obtain optimal model generalization; 3) exact inverse solution for output weights is guaranteed under invertible kernel matrix, resulting to smaller reconstruction error; and 4) all transformation matrices are unified into two matrices only, so that storage can be reduced and may shorten model execution time. Benchmark data sets of different sizes have been employed for the evaluation of ML-KELM. Experimental results have verified the contributions of the proposed ML-KELM. The improvement in accuracy over benchmark data sets is up to 7%. |
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| AbstractList | Recently, multilayer extreme learning machine (ML-ELM) was applied to stacked autoencoder (SAE) for representation learning. In contrast to traditional SAE, the training time of ML-ELM is significantly reduced from hours to seconds with high accuracy. However, ML-ELM suffers from several drawbacks: 1) manual tuning on the number of hidden nodes in every layer is an uncertain factor to training time and generalization; 2) random projection of input weights and bias in every layer of ML-ELM leads to suboptimal model generalization; 3) the pseudoinverse solution for output weights in every layer incurs relatively large reconstruction error; and 4) the storage and execution time for transformation matrices in representation learning are proportional to the number of hidden layers. Inspired by kernel learning, a kernel version of ML-ELM is developed, namely, multilayer kernel ELM (ML-KELM), whose contributions are: 1) elimination of manual tuning on the number of hidden nodes in every layer; 2) no random projection mechanism so as to obtain optimal model generalization; 3) exact inverse solution for output weights is guaranteed under invertible kernel matrix, resulting to smaller reconstruction error; and 4) all transformation matrices are unified into two matrices only, so that storage can be reduced and may shorten model execution time. Benchmark data sets of different sizes have been employed for the evaluation of ML-KELM. Experimental results have verified the contributions of the proposed ML-KELM. The improvement in accuracy over benchmark data sets is up to 7%. |
| Author | Wong, Chi Man Wong, Pak Kin Cao, Jiuwen Vong, Chi Man |
| Author_xml | – sequence: 1 givenname: Chi Man surname: Wong fullname: Wong, Chi Man email: mb55501@umac.mo organization: Department of Computer and Information Science, University of Macau, Macau, China – sequence: 2 givenname: Chi Man orcidid: 0000-0001-7997-8279 surname: Vong fullname: Vong, Chi Man email: cmvong@umac.mol organization: Department of Computer and Information Science, University of Macau, Macau, China – sequence: 3 givenname: Pak Kin surname: Wong fullname: Wong, Pak Kin email: fstpkw@umac.mo organization: Department of Electromechanical Engineering, University of Macau, Macau, China – sequence: 4 givenname: Jiuwen surname: Cao fullname: Cao, Jiuwen email: jwcao@hdu.edu.cn organization: Institute of Information and Control, Hangzhou Dianzi University, Hangzhou, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/28055922$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | Benchmark testing Benchmarks Datasets Kernel Kernel learning Learning Learning algorithms Learning systems Manuals multilayer extreme learning machine (ML-ELM) Neural networks Nonhomogeneous media Reconstruction representation learning Representations stacked autoencoder (SAE) Training Tuning |
| Title | Kernel-Based Multilayer Extreme Learning Machines for Representation Learning |
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