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Optimal subspace classification method for complex data

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Abstract

KNNModel algorithm is an improved version for k-nearest neighbor method. However, it has the problem of high time complexity and lower performance when dealing with complex data. An optimal subspace classification method called IKNNModel is proposed in this paper by projecting different training samples onto their own optimal subspace and constructing the corresponding class cluster and pure cluster as the basis of classification. For datasets with complex structure, that is, the training samples from different categories are overlapped with one another on the original space or have a high dimensionality, the proposed method can construct the corresponding clusters for the overlapped samples on their own subspaces easily. Experimental results show that compared with KNNModel, the proposed method not only significantly improves the classification performance on datasets with complex structure, but also improves the efficiency of the classification.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China under Grant No. 61070062, the key Scientific Research Project on the Cooperation of Industry and University of Fujian Province of China under Grant No. 2010H6007 and the key Scientific Research Project of the Higher Education Institutions of Fujian Province of China under Grant No. JK2009006.

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Authors and Affiliations

  1. School of Mathematics and Computer Science, Fujian Normal University, Fuzhou, China

    Nan Li, Gong-De Guo, Li-Fei Chen & Si Chen

  2. Key Laboratory of Network Security and Cryptography, Fujian Normal University, Fuzhou, China

    Nan Li, Gong-De Guo, Li-Fei Chen & Si Chen

Authors
  1. Nan Li
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  2. Gong-De Guo
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  3. Li-Fei Chen
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  4. Si Chen
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Correspondence to Gong-De Guo.

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Li, N., Guo, GD., Chen, LF. et al. Optimal subspace classification method for complex data. Int. J. Mach. Learn. & Cyber. 4, 163–171 (2013). https://doi.org/10.1007/s13042-012-0080-1

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  • DOI: https://doi.org/10.1007/s13042-012-0080-1

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