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Fuzzy Local Mean Discriminant Analysis for Dimensionality Reduction

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Abstract

“Fuzzy set” theory can effectively manage the vagueness and ambiguity of the images being degraded by poor illumination component. In this study, we augment mechanism of “fuzzy set” into the algorithm design, and propose fuzzy local mean discriminant analysis (FLMDA) for dimensionality reduction. In FLMDA, the nearest neighborhoods are selected as the local patches. On each local patch, FLMDA redefines the fuzzy local class-means and then constructs the fuzzy local between-class and within-class scatters, respectively. By maximizing the difference of fuzzy local between-class scatter and fuzzy local within-class scatter, FLMDA finds the optimal transformed subspace, in which the local neighbor relationship is preserved while at the same time the compactness and separability are enhanced. The experimental results on the AR face database, Yale face database, UCI Wine dataset and PolyU palmprint database show that FLMDA outperforms the state-of-the-art algorithms.

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Acknowledgments

This work was partially supported by the National Nature Science Foundation of China (Grant Nos. 61305036, 61322306, 61333013, and 61273192), the China Postdoctoral Science Foundation funded project (Grant 2014M560657 and 2015T80898), and Shenzhen Council for Scientific and Technological Innovation (Grant JCYJ20150324141711637), Scientific Funds approved in 2013 for Higher Level Talents by Guangdong Provincial universities and Project supported by GDHVPS 2014.

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Correspondence to Jie Xu.

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Xu, J., Gu, Z. & Xie, K. Fuzzy Local Mean Discriminant Analysis for Dimensionality Reduction. Neural Process Lett 44, 701–718 (2016). https://doi.org/10.1007/s11063-015-9489-3

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  • DOI: https://doi.org/10.1007/s11063-015-9489-3

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