Share Email Print

Proceedings Paper

Extreme learning machine with variance inflation factor for robust pattern recognition
Author(s): Paheding Sidike; Almabrok Essa; Maher Qumsiyeh; Vijayan Asari
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Extreme learning machine (ELM), as a single hidden layer feedforward neural network, has shown very effective performance in pattern analysis and machine intelligence; however, there are some limitations that constrain the performance of ELM, such as data multicollinearity issues. The generalization capability of ELM could be significantly deteriorated when multicollinearity is present in the hidden layer output matrix which causes the matrix to become singular or ill-conditioning. To overcome such a problem, ridge regression can be utilized. The conventional way to avoid multicollinearity in ELM is achieved by precisely adjusting the ridge constant, which may not be a sophisticate solution to obtain the optimal value. In this paper, we present a solution for finding a satisfactory ridge constant by incorporating variance inflation factors (VIF) during calculating output weights in ELM, we termed this technique as ELM-VIF. Experimental results on handwritten digit recognition show that the proposed ELM-VIF, compared with the original ELM, has better stability and generalization performance.

Paper Details

Date Published: 10 October 2017
PDF: 6 pages
Proc. SPIE 10203, Pattern Recognition and Tracking XXVIII, 102030K (10 October 2017); doi: 10.1117/12.2264422
Show Author Affiliations
Paheding Sidike, Univ. of Dayton (United States)
Almabrok Essa, Univ. of Dayton (United States)
Maher Qumsiyeh, Univ. of Dayton (United States)
Vijayan Asari, Univ. of Dayton (United States)

Published in SPIE Proceedings Vol. 10203:
Pattern Recognition and Tracking XXVIII
Mohammad S. Alam, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?