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首页> 外文会议>2017 IEEE 15th International Conference on Dependable, Autonomic and Secure Computing, 2017 IEEE 15th International Conference on Pervasive Intelligence and Computing, 2017 IEEE 3rd International Conference on Big Data Intelligence and Computing, 2017 IEEE Cyber Science and Technology Congress >A High-Dimensional Outlier Detection Algorithm Base on Relevant Subspace
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A High-Dimensional Outlier Detection Algorithm Base on Relevant Subspace

机译：基于相关子空间的高维离群值检测算法

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摘要

Outlier detection in high-dimensional big data is an important data mining task to distinguish outliers from regular objects. In tradition, outlier detection approaches miss outliers which hide in full data space. However, these methods are deteriorated due to the notorious "curse of dimensionality" which leads to distance cannot express the deviation of outlier and normal objects, and the exponential computation leads to low efficiency. In this paper, we propose an outlier detection method based on relevant subspace, which can effectively describe the local distribution of objects and detect outliers hidden in subspaces of the data. In thorough experiments on synthetic data and real data, it shows that the method outperforms competing outlier ranking approaches by detecting outliers in subspace.
机译：高维大数据中的异常值检测是一项重要的数据挖掘任务，用于将异常值与常规对象区分开。传统上，离群值检测方法会遗漏隐藏在完整数据空间中的离群值。但是，这些方法由于臭名昭著的“维数诅咒”而恶化，导致距离无法表达离群值和法线对象的偏差，并且指数计算导致效率低下。本文提出了一种基于相关子空间的离群值检测方法，该方法可以有效地描述对象的局部分布，并检测出隐藏在数据子空间中的离群值。在对合成数据和真实数据进行的全面实验中，该方法通过检测子空间中的离群值，其性能优于竞争的离群值排名方法。

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