Abstract
Linear discriminant analysis (LDA) is a very popular supervised classification approach. But it cannot perform well in some cases such as large sample size, etc. In terms of its shortcoming, some scholars in this area put up the idea of subclass, which can break out of LDA’s limitation and achieve better classification results. Subclass discriminant analysis (SDA) worked out the division of subclasses, before solving the generalized eigenvalue problem. By contrast, our proposed approach performs subclass division based on K-means cluster, class by class, in the iterative steps under EM framework. The experimental results on two character databases show that our proposed approach can achieve better results than SDA, meanwhile not quite time-consuming.
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Tao, Y., Yang, J. (2012). Enhanced Iterative Projection for Subclass Analysis under EM Framework. In: Liu, CL., Zhang, C., Wang, L. (eds) Pattern Recognition. CCPR 2012. Communications in Computer and Information Science, vol 321. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33506-8_1
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DOI: https://doi.org/10.1007/978-3-642-33506-8_1
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