Analysis on fast training speed of extreme learning machine and replacement policy
by Shi-Xin Zhao; Xi-Zhao Wang; Li-Ying Wang; Jun-Mei Hu; Wei-Ping Li
International Journal of Wireless and Mobile Computing (IJWMC), Vol. 13, No. 4, 2017

Abstract: Extreme learning machine is known for its fast learning speed while maintaining acceptable generalisation. Its learning process can be divided into two parts: (1) randomly assigns input weights and biases in hidden layer, and (2) analytically determines output weights by the use of Moore-Penrose generalised inverse. Through the analysis from theory and experiment aspects we point out that it is the random weights assignment rather than the analytical determination with generalised inverse that leads to its fast training speed. In fact, the calculation of generalised inverse of hidden layer output matrix based on singular value decomposition (SVD) has very low efficiency especially on large scale data, and even directly cannot work. Considering this high calculation complexity reduces the learning speed of ELM conjugate gradient is introduced as a replacement of Moore-Penrose generalised inverse and conjugate gradient based ELM (CG-ELM) is proposed. Numerical simulations show that, in most cases, CG-ELM achieved faster speed than ELM in the condition of maintaining similar generalisation. Even in the case that ELM cannot work because of the huge amount of data CG-ELM attains good performance, which illustrates that Moore-Penrose generalised inverse is not the contribution of fast learning speed of ELM from experiment view.

Online publication date: Wed, 17-Jan-2018

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Wireless and Mobile Computing (IJWMC):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com