Journal
IEEE ACCESS
Volume 7, Issue -, Pages 119181-119191Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2924647
Keywords
Extreme learning machine; deep learning; tensor; stacking; pattern classification
Categories
Funding
- National Key R&D Program of China [2018YFC0825305, 2018YFC0825303]
- State Key Program of National Natural Science of China [61836009]
- National Natural Science Foundation of China [61501353, 61573267, 61473215]
- Natural Science Pre-Research Fund of Shaanxi University of Science and Technology [2019BJ-11]
- Program for Cheung Kong Scholars and Innovative Research Team in University [IRT_15R53]
- Fund for Foreign Scholars in University Research and Teaching Programs (the 111 Project) [B07048]
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A novel deep architecture, the sparse deep tensor extreme learning machine (SDT-ELM), is presented as a tool for pattern classification. In extending the original ELM, the proposed SDT-ELM gains the theoretical advantage of effectively reducing the number of hidden-layer parameters by using tensor operations, and using a weight tensor to incorporate higher-order statistics of the hidden feature. In addition, the SDT-ELM gains the implementation advantage of enabling the random hidden nodes to be added block by block, with all blocks having the same hidden layer configuration. Moreover, an SDT-ELM without randomness can also achieve better learning accuracy. Extensive experiments with three widely used classification datasets demonstrate that the proposed algorithm achieves better generalization performance.
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