Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 396780, 7 pages
http://dx.doi.org/10.1155/2013/396780
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.

Linked References

  1. R. A. Fisher, “The use of multiple measurements in taxonomic problems,” Annals of Eugenics, vol. 7, pp. 178–188, 1936. View at Google Scholar
  2. J. Kittler, “On the discriminant Vector method of feature selection,” IEEE Transactions on Computers, vol. 26, no. 6, pp. 604–606, 1977. View at Google Scholar
  3. K. Kira and L. A. Rendell, “The feature selection problem: traditional methods and a new algorithm,” in Proceedings of the 10th National Conference on Artificial Intelligence, pp. 129–134, July 1992. View at Scopus
  4. I. Kononenko, “Estimating attributes: analysis and extension of RELIEF,” Proceedings of the European Conference on Machine Learning, pp. 171–182, 1994. View at Google Scholar
  5. L. Yu and H. Liu, “Efficiently handling feature redundancy in high-dimensional data,” in Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 685–690, August 2003. View at Publisher · View at Google Scholar · View at Scopus
  6. G. V. Lashkia and L. Anthony, “Relevant, irredundant feature selection and noisy example elimination,” IEEE Transactions on Systems, Man, and Cybernetics B, vol. 34, no. 2, pp. 888–897, 2004. View at Publisher · View at Google Scholar · View at Scopus
  7. Q. Gu, Z. Li, and J. Han, “Generalized fisher score for feature selection,” in Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence (UAI '11), pp. 266–273, Barcelona, Spain, July 2011. View at Scopus
  8. A. R. Webb, Statistical Pattern Recognition, John Wiley & Sons, New York, NY, USA, 2nd edition, 2002.
  9. M. Dash, H. Liu, and J. Yao, “Dimensionality reduction of unsupervised data,” in Proceedings of the 9th IEEE International Conference on Tools with Artificial Intelligence, pp. 532–539, November 1997. View at Scopus
  10. J. Basak, R. K. De, and S. K. Pal, “Unsupervised feature selection using a neuro-fuzzy approach,” Pattern Recognition Letters, vol. 19, no. 11, pp. 997–1006, 1998. View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  11. P. Mitra, C. A. Murthy, and S. K. Pal, “Unsupervised feature selection using feature similarity,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 3, pp. 301–312, 2002. View at Publisher · View at Google Scholar · View at Scopus
  12. M. Dash, K. Choi, P. Scheuermann, and H. Liu, “Feature selection for clustering—a filter solution,” in Proceedings of the 2nd IEEE International Conference on Data Mining, pp. 115–122, December 2002. View at Scopus
  13. S. Y. M. Shi and P. N. Suganthan, “Unsupervised similarity-based feature selection using heuristic Hopfield neural networks,” in Proceedings of the International Joint Conference on Neural Networks, vol. 3, pp. 1838–1843, July 2003. View at Scopus
  14. J. G. Dy and C. E. Brodley, “Feature selection for unsupervised learning,” Journal of Machine Learning Research, vol. 5, pp. 845–889, 2004. View at Google Scholar · View at Zentralblatt MATH
  15. S. Z. Li and A. K. Jain, Encyclopedia of Biometrics, Springer, 2009.
  16. S.-Q. Cao, S.-T. Wang, X.-F. Chen, Z.-P. Xie, and Z.-H. Deng, “Fuzzy fisher criterion based semi-fuzzy clustering algorithm,” Journal of Electronics & Information Technology, vol. 30, no. 9, pp. 2162–2165, 2008. View at Google Scholar · View at Scopus
  17. X.-B. Zhi and J.-L. Fan, “Fuzzy fisher criterion based adaptive dimension reduction fuzzy clustering algorithm,” Journal of Electronics & Information Technology, vol. 31, no. 11, pp. 2653–2658, 2009. View at Google Scholar · View at Scopus
  18. C. L. Blake and C. J. Merz, “UCI repository of machine learning databases,” Department of Information and Computer Science, University of California, Irvine, Calif, USA, 1998, http://archive.ics.uci.edu/ml/.
  19. W. Rand, “Objective criteria for the evaluation of clustering methods,” Journal of the American Statistical Association, vol. 66, no. 336, pp. 846–850, 1971. View at Google Scholar
  20. Center for Machine Learning and Intelligent Systems, the University of California, Irvine, Calif, USA, 2011, http://archive.ics.uci.edu/ml/datasets/Steel+Plates+Faults.
  21. M. Buscema, S. Terzi, and W. Tastle, “A new meta-classifier,” in Proceedings of the Annual North American Fuzzy Information Processing Society Conference (NAFIPS' 10), pp. 1–7, IEEE Press, Toronto, Canada, July 2010. View at Publisher · View at Google Scholar · View at Scopus
  22. J. H. Manton, “Differential calculus, tensor products and the importance of notation,” 2013, http://arxiv.org/abs/1208.0197.
  23. J. Wilde, Unconstrained Optimization, 2011, http://www.econ.brown.edu/students/Takeshi_Suzuki/Math_Camp_2011/Unconstrained_Optimization-2011.pdf.