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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Maximum Correntropy Criterion-Based Hierarchical One-Class Classification
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Maximum Correntropy Criterion-Based Hierarchical One-Class Classification

机译：基于最大的正管标准的分层单级分类

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摘要

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.
机译：由于异常/异常检测的有效性，过去已经广泛研究了一流的算法。该代表包括浅结构方法和深网络，如单级支持向量机（OC-SVM），单级极限学习机（OC-ELM），深度支持矢量数据描述（深度SVDD），和多层oc-elm（ml-ocelm / mk-ocelm）。然而，现有的算法通常基于最小均方误差（MSE）标准，这对于高斯噪声具有鲁棒，但在处理大异常值方面不太有效。为了缓解这种缺陷，首先提出了一种基于稳健的最大控制标准（MCC），然后进一步扩展到分层网络，以增强其特征复杂和大数据的能力（名为HC-Ocelm） 。采用与输出权重优化采用与固定点迭代更新方案组合的梯度推导。对许多基准数据集进行实验进行有效验证。提供了许多最先进的方法的比较为优势演示。

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