Sparse Deep Tensor Extreme Learning Machine for Pattern Classification

IEEE Access

Jin Zhao¹  Licheng Jiao¹ 

[1] Key Laboratory of Intelligent Perception and Image Understanding, School of Artificial Intelligence, Ministry of Education, Xidian University, China;

关键词: Extreme learning machine;    deep learning;    tensor;    stacking;    pattern classification;   

DOI  :  10.1109/ACCESS.2019.2924647

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown