Abstract
In recent years, pattern analysis plays an important role in data mining and recognition, and many variants have been proposed to handle complicated scenarios. In the literature, it has been quite familiar with high dimensionality of data samples, but either such characteristics or large data sets have become usual sense in real-world applications. In this work, an improved maximum margin criterion (MMC) method is introduced firstly. With the new definition of MMC, several variants of MMC, including random MMC, layered MMC, 2D\( ^2 \) MMC, are designed to make adaptive learning applicable. Particularly, the MMC network is developed to learn deep features of images in light of simple deep networks. Experimental results on a diversity of data sets demonstrate the discriminant ability of proposed MMC methods are component to be adopted in complicated application scenarios.
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Notes
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The implementations are available at: http://mch.one/resources.
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Acknowledgements
The authors would like to thank Universität zu Lübeck for sparse coding data set of MNIST, and the Chinese University of Hong Kong for target coding data set of STL-10. The corresponding author of this work is Dr. Miao Cheng.
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Cheng, M., Liu, Z., Zou, H., Tsoi, A.C. (2018). A Family of Maximum Margin Criterion for Adaptive Learning. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11303. Springer, Cham. https://doi.org/10.1007/978-3-030-04182-3_33
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