Densely Connected Deep Extreme Learning Machine Algorithm

© 2020, Springer Science+Business Media, LLC, part of Springer Nature. As a single hidden layer feed-forward neural network, the extreme learning machine (ELM) has been extensively studied for its short training time and good generalization ability. Recently, with the deep learning algorithm becoming a research hotspot, some deep extreme learning machine algorithms such as multi-layer extreme learning machine (ML-ELM) and hierarchical extreme learning machine (H-ELM) have also been proposed. However, the deep ELM algorithm also has many shortcomings: (1) when the number of model layers is shallow, the random feature mapping makes the sample features cannot be fully learned and utilized; (2) when the number of model layers is deep, the validity of the sample features will decrease after continuous abstraction and generalization. In order to solve the above problems, this paper proposes a densely connected deep ELM algorithm: dense-HELM (D-HELM). Benchmark data sets of different sizes have been employed for the property of the D-HELM algorithm. Compared with the H-ELM algorithm on the benchmark dataset, the average test accuracy is increased by 5.34% and the average training time is decreased by 21.15%. On the NORB dataset, the proposed D-HELM algorithm still maintains the best classification results and the fastest training speed. The D-HELM algorithm can make full use of the features of hidden layer learning by using the densely connected network structure and effectively reduce the number of parameters. Compared with the H-ELM algorithm, the D-HELM algorithm significantly improves the recognition accuracy and accelerates the training speed of the algorithm