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Maximum inter-class and marginal discriminant embedding (MIMDE) for feature extraction and classification

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Abstract

This paper develops a supervised discriminant technique based on graph embedding, called maximum inter-class and marginal discriminant embedding (MIMDE) for feature and classification in high-dimensional data space. In graph embedding, our objective is to find a linear transform matrix to make the samples in the same class as compact as possible and the samples belong to the different classes as dispersed as possible through local property (intra-class compactness graph and inter-class separability graph) and marginal property (margin separability graph). This characteristic makes MIMDE more intuitive and more powerful than marginal Fisher analysis which not considered inter-class separability graph. Experimental results on ORL and AR face databases show the effectiveness of the proposed method.

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Acknowledgments

This work is partially supported by the National Science Foundation of China under Grant No. 60632050, 90820306, 60873151, 60973098, 61005008, and 61005005.

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Authors and Affiliations

  1. School of Information Engineering, Nanchang Hangkong University, Nanchang, 330063, China

    Minghua Wan

  2. Key Laboratory of Nondestructive Testing, Nanchang Hangkong University, Ministry of Education, Nanchang, 330063, China

    Minghua Wan

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  1. Minghua Wan
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Correspondence to Minghua Wan.

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Wan, M. Maximum inter-class and marginal discriminant embedding (MIMDE) for feature extraction and classification. Neural Comput & Applic 21, 1737–1743 (2012). https://doi.org/10.1007/s00521-011-0763-7

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  • DOI: https://doi.org/10.1007/s00521-011-0763-7

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