1. Maximum Correntropy Criterion-Based Hierarchical One-Class Classification.
- Author
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Cao, Jiuwen, Dai, Haozhen, Lei, Baiying, Yin, Chun, Zeng, Huanqiang, and Kummert, Anton
- Subjects
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SUPPORT vector machines , *VECTOR data , *MACHINE learning , *RANDOM noise theory , *OUTLIER detection , *CLASSIFICATION - Abstract
Due to the effectiveness of anomaly/outlier detection, one-class algorithms have been extensively studied in the past. The representatives include the shallow-structure methods and deep networks, such as the one-class support vector machine (OC-SVM), one-class extreme learning machine (OC-ELM), deep support vector data description (Deep SVDD), and multilayer OC-ELM (ML-OCELM/MK-OCELM). However, existing algorithms are generally built on the minimum mean-square-error (mse) criterion, which is robust to the Gaussian noises but less effective in dealing with large outliers. To alleviate this deficiency, a robust maximum correntropy criterion (MCC)-based OC-ELM (MC-OCELM) is first proposed and then further extended to a hierarchical network to enhance its capability in characterizing complex and large data (named HC-OCELM). The gradient derivation combining with a fixed-point iterative updation scheme is adopted for the output weight optimization. Experiments on many benchmark data sets are conducted for effectiveness validation. Comparisons to many state-of-the-art approaches are provided for the superiority demonstration. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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