Abstract
In this paper, we propose a new classification method using composite features, each of which consists of a number of primitive features. The covariance of two composite features contains information on statistical dependency among multiple primitive features. A new discriminant analysis (C-LDA) using the covariance of composite features is a generalization of the linear discriminant analysis (LDA). Unlike LDA, the number of extracted features can be larger than the number of classes in C-LDA. Experimental results on several data sets indicate that C-LDA provides better classification results than other methods.
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References
Jain, A.K., Duin, R.P.W., Mao, J.: Statistical Pattern Recognition: A Review. IEEE Trans. Pattern Analysis and Machine Intelligence 22, 4–37 (2000)
Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press, New York (1990)
Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Trans. Pattern Analysis and Machine Intelligence 19, 711–720 (1997)
Cevikalp, H., Neamtu, M., Wilkes, M., Barkana, A.: Discriminative Common Vectors for Face Recognition. IEEE Trans. Pattern Analysis and Machine Intelligence 27, 4–13 (2005)
Ye, J., Li, Q.: A Two-Stage Linear Discriminant Analysis via QR-Decomposition. IEEE Trans. Pattern Analysis and Machine Intelligence 27, 929–941 (2005)
Fukunaga, K., Mantock, J.M.: Nonparametric Discriminant Analysis. IEEE Trans. Pattern Analysis and Machine Intelligence 5, 671–678 (1983)
Brunzell, H., Eriksson, J.: Feature Reduction for Classification of Multidimensional Data. Pattern Recognition 33, 1741–1748 (2000)
Loog, M., Duin, R.P.W.: Linear Dimensionality Reduction via a Heteroscedastic Extension of LDA: The Chernoff Criterion. IEEE Trans. Pattern Analysis and Machine Intelligence 26, 732–739 (2004)
Chen, C.H.: On Information and Distance Measures, Error Bounds, and Feature Selection. Information Sciences 10, 159–173 (1976)
Yang, J., Zhang, D., Yong, X., Yang, J.-y.: Two-dimensional Discriminant Transform for Face Recognition. Pattern Recognition 38, 1125–1129 (2005)
Yang, J., Yang, J.-y.: From image vector to matrix: a straightforward image projection technique-IMPCA vs. PCA. Pattern Recognition 35, 1997–1999 (2002)
Newman, D.J., Hettich, S., Blake, C.L., Merz, C.J.: UCI Repository of Machine Learning Databases (1998), http://www.ics.uci.edu/mlearn/MLRepository.html
Papadimitriou, C.H., Steiglitz, K.: Combinatorial Optimization: Algorithms and Complexity. Prentice-Hall, Englewood Cliffs (1982)
Kwak, N., Choi, C.-H.: Input Feature Selection for Classification Problems. IEEE Trans. Neural Networks 13, 143–159 (2002)
Webb, A.: Statistical Pattern Recognition, 2nd edn. Wiley, West Sussex (2002)
Moghaddam, B., Pentland, A.: Probabilistic Visual Learning for Object Representation. IEEE Trans. Pattern Analysis and Machine Intelligence 19, 696–710 (1997)
Fukunaga, K., Hummels, D.M.: Bayes Error Estimation Using Parzen and k-NN Procedures. IEEE Trans. Pattern Analysis and Machine Intelligence 9, 634–643 (1987)
Parzen, E.: On Estimation of a Probability Density Function andMode. The Annals of Mathematical Statistics 33, 1065–1076 (1962)
Kim, C., Oh, J., Choi, C.-H.: Combined Subspace Method Using Global and Local Features for Face Recognition. In: Proc. Int’l Joint Conf. Neural Networks, pp. 2030–2035 (2005)
Veenman, C.J., Reinders, M.J.T.: The Nearest Subclass Classifier: A Compromise Between the Nearest Mean and Nearest Neighbor Classifier. IEEE Trans. Pattern Analysis and Machine Intelligence 27, 1417–1429 (2005)
Toh, K.-A., Tran, Q.-L., Srinivasan, D.: Benchmarking a Reduced Multivariate Polynomial Pattern Classifier. IEEE Trans. Pattern Analysis and Machine Intelligence 26, 740–755 (2004)
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Kim, C., Choi, CH. (2006). Pattern Classification Using Composite Features. In: Kollias, S., Stafylopatis, A., Duch, W., Oja, E. (eds) Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, vol 4132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840930_47
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DOI: https://doi.org/10.1007/11840930_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-38871-5
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