Densely Connected Deep Extreme Learning Machine Algorithm
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Densely Connected Deep Extreme Learning Machine Algorithm X. W. Jiang 1 & T. H. Yan 1
&
J. J. Zhu 1 & B. He 2 & W. H. Li 3 & H. P. Du 3 & S. S. Sun 3
Received: 23 May 2018 / Accepted: 3 July 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract 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 DHELM 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. Keywords Extreme learning machine (ELM) . Densely connections . Deep learning . Representation learning
Introduction Since the concept of extreme learning machine (ELM) algorithm had been proposed in 2006 [1], ELM algorithm has rapidly become an attractive area of research spot in the field of machine learning and artificial intelligence research, after continuous research and development by scholars. In recent years, extreme learning machine algorithm based on kernel function (K-ELM) [2], multi-layer extreme learning machine algorithm (ML-ELM) [3], hierarchical extreme learning machine algorithm (H-ELM) [4], local receptive field-based ELM (ELM-LRF) [5], and kernel multi-layer extreme
* T. H. Yan [email protected]; [email protected] 1
School of Mechanical & Electrical Engineering, China Jiliang University, Hangzhou, China
2
School of Information Science & Engineering, Ocean University of China, Qingdao, Shandong, China
3
Faculty of Engineering & Information System, University of Wollongong, Wollongong, NSW 2522, Australia
learning machine algorithm (ML-KELM) [6] have been successively proposed. They are widely used in various fields such as image classif
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