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Sparse Deep Tensor Extreme Learning Machine for Pattern Classification
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DOI：10.1109/ACCESS.2019.2924647.png)
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摘要
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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.
Keyword:
Extreme learning machine
deep learning
tensor
stacking
pattern classification
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期刊
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3.6
论文数:
9.8W
被引数:
29.4W
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