Abstract
A nonlinear version of discriminative elastic embedding (DEE) algorithm is presented, called kernel discriminative elastic embedding (KDEE). In this paper, we concretely fulfill the following works: (1) class labels and linear projection matrix are integrated into the kernel-based objective function; (2) two different strategies are adopted for optimizing the objective function of KDEE, and accordingly the final algorithms are termed as KDEE1 and KDEE2 respectively; (3) a deliberately selected Laplacian search direction is adopted in KDEE1 for faster convergence. Experimental results on several publicly available databases demonstrate that the proposed algorithm achieves powerful pattern revealing capability for complex manifold data.
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Acknowledgments
The authors would like to thank the anonymous reviewers for their constructive comments and suggestions. This project was supported in part by the Provincial Science Foundation of Zhejiang(LY15F030014), National Natural Science Foundation of China(61379123), Zhejiang Provincial Natural Science Foundation(LY13F030011), Zhejiang Provincial Natural Science Foundation(LQ14F030003), The School of Natural Science Foundation(1401119023408) and National Science and Technology Support Plan(2012BAD10B0101).
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Zheng, J., Qiu, H., Wang, W. et al. Kernel-based discriminative elastic embedding algorithm. Appl Intell 44, 449–456 (2016). https://doi.org/10.1007/s10489-015-0709-3
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DOI: https://doi.org/10.1007/s10489-015-0709-3