ODHD: one-class brain-inspired hyperdimensional computing for outlier detection
ODHD: one-class brain-inspired hyperdimensional computing for outlier detection
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DOI：
10.1145/3489517.3530395
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发表时间：
2022-07
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影响因子：
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通讯作者：
Ruixuan Wang;Xun Jiao;X. S. Hu
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作者：
Ruixuan Wang;Xun Jiao;X. S. Hu
异常值检测是一种经典且重要的技术，已被应用于医疗诊断和物联网等不同的应用领域。最近，基于机器学习的异常值检测算法，例如一类支持向量机（OCSVM）、隔离森林和自动编码器，在异常值检测方面表现出了良好的效果。在本文中，我们与这些经典学习方法截然不同，提出了 ODHD，一种基于超维计算（HDC）的异常值检测方法。在 ODHD 中，异常值检测过程基于 P-U 学习结构，其中我们基于内部样本训练一类 HV。这个HV代表了所有inlier样本的抽象信息；因此，任何其相应 HV 与该 HV 不同的（测试）样本都将被视为异常值。我们使用跨不同应用领域的六个数据集进行了广泛的评估，并使用包括准确度、F1 分数和 ROC-AUC 在内的三个指标将 ODHD 与多种基线方法（包括 OCSVM、隔离森林和自动编码器）进行比较。实验结果表明，ODHD 在每个数据集的每个指标上都优于所有基线方法。此外，我们对 ODHD 进行了设计空间探索，以说明性能和效率之间的权衡。本文提出的有希望的结果为异常值检测的传统学习算法提供了可行的选择和替代方案。
Outlier detection is a classical and important technique that has been used in different application domains such as medical diagnosis and Internet-of-Things. Recently, machine learning-based outlier detection algorithms, such as one-class support vector machine (OCSVM), isolation forest and autoencoder, have demonstrated promising results in outlier detection. In this paper, we take a radical departure from these classical learning methods and propose ODHD, an outlier detection method based on hyperdimensional computing (HDC). In ODHD, the outlier detection process is based on a P-U learning structure, in which we train a one-class HV based on inlier samples. This HV represents the abstraction information of all inlier samples; hence, any (testing) sample whose corresponding HV is dissimilar from this HV will be considered as an outlier. We perform an extensive evaluation using six datasets across different application domains and compare ODHD with multiple baseline methods including OCSVM, isolation forest, and autoencoder using three metrics including accuracy, F1 score and ROC-AUC. Experimental results show that ODHD outperforms all the baseline methods on every dataset for every metric. Moreover, we perform a design space exploration for ODHD to illustrate the tradeoff between performance and efficiency. The promising results presented in this paper provide a viable option and alternative to traditional learning algorithms for outlier detection.