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Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 396780, 7 pages
Research Article

Unsupervised Optimal Discriminant Vector Based Feature Selection Method

1Faculty of Mechanical Engineering, Huaiyin Institute of Technology, Huai’an 223003, China
2Department of Electrical and Electronic Engineering, University of Melbourne, Victoria, VIC 3010, Australia

Received 22 July 2013; Accepted 21 September 2013

Academic Editor: Baochang Zhang

Copyright © 2013 Su-Qun Cao and Jonathan H. Manton. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


An efficient unsupervised feature selection method based on unsupervised optimal discriminant vector is developed to find the important features without using class labels. Features are ranked according to the feature importance measurement based on unsupervised optimal discriminant vector in the following steps. First, fuzzy Fisher criterion is adopted as objective function to derive the optimal discriminant vector in unsupervised pattern. Second, the feature importance measurement based on elements of unsupervised optimal discriminant vector is defined to determine the importance of each feature. The features with little importance measurement are removed from the feature subset. Experiments on UCI dataset and fault diagnosis are carried out to show that the proposed method is very efficient and able to deliver reliable results.