Journal
IEEE TRANSACTIONS ON CYBERNETICS
Volume 46, Issue 5, Pages 1229-1241Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2015.2434841
Keywords
Extreme learning machine (ELM); inverse-free; neural networks; optimal updates
Categories
Funding
- National Natural Science Foundation of China [61401385, 61373086, 61202347, 61375047]
- Young Scientist Foundation of Chongqing [cstc2014kjrc-qnrc40005]
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The extreme learning machine (ELM) has drawn insensitive research attentions due to its effectiveness in solving many machine learning problems. However, the matrix inversion operation involved in the algorithm is computational prohibitive and limits the wide applications of ELM in many scenarios. To overcome this problem, in this paper, we propose an inverse-free ELM to incrementally increase the number of hidden nodes, and update the connection weights progressively and optimally. Theoretical analysis proves the monotonic decrease of the training error with the proposed updating procedure and also proves the optimality in every updating step. Extensive numerical experiments show the effectiveness and accuracy of the proposed algorithm.
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