Extended Fisher Criterion Based on Auto-correlation Matrix Information
Fisher’s linear discriminant analysis (FLDA) has been attracting many researchers and practitioners for several decades thanks to its ease of use and low computational cost. However, FLDA implicitly assumes that all the classes share the same covariance: which implies that FLDA might fail when this assumption is not necessarily satisfied. To overcome this problem, we propose a simple extension of FLDA that exploits a detailed covariance structure of every class by utilizing revealed by the class-wise auto-correlation matrices. The proposed method achieves remarkable improvements classification accuracy against FLDA while preserving two major strengths of FLDA: the ease of use and low computational costs. Experimental results with MNIST and other several data sets in UCI machine learning repository demonstrate the effectiveness of our method.
KeywordsStatistical Pattern Recognition Discriminant Axis MNIST Dataset Discriminative Feature Extractor Simple Matrix Operation
- 1.Fisher, R.A.: The use of multiple measurements in taxonomic problems. Annals of Eigenics 7, 179–188 (1936)Google Scholar
- 2.Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis. John Willey and Sons (1973)Google Scholar
- 5.Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press (1990)Google Scholar
- 14.Watanabe, S., Lambert, P.F., Kulikowski, C.A., Buxton, J.L., Walker, R.: Evaluation and selection of variables in pattern recognition. Comp. & Info. Sciences 2, 91–122 (1967)Google Scholar
- 15.Lim, G., Park, C.H.: Semi-supervised Dimension Reduction Using Graph-Based Discriminant Analysis. In: 2009 Ninth IEEE International Conference on Computer and Information Technology, pp. 9–13 (2009)Google Scholar