Abstract
In this paper, we propose a novel one class learning method for the large scale data. In the context of one class learning, the proposed method could automatically learn the appropriate number of prototypes needed to represent the original target examples, and acquire the essential topology structure of target distribution. Then based on the learned topology structure, a neighbors analysis technique is utilized to separate the target examples from outlier examples. Experimental results show that our method can accommodate the large scale data environment, and achieve comparable or preferable performance than other contemporary methods on both artificial and real word data sets.
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Acknowledgement
The authors would like to thank the anonymous reviewers for their time and valuable suggestions. This work is supported in part by the National Science Foundation of China under Grant Nos. (61375064, 61373001) and Jiangsu NSF grant (BK20131279).
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Deng, Q., Yang, Y., Shen, F., Luo, C., Zhao, J. (2016). An Incremental One Class Learning Framework for Large Scale Data. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9948. Springer, Cham. https://doi.org/10.1007/978-3-319-46672-9_46
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DOI: https://doi.org/10.1007/978-3-319-46672-9_46
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