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Online Data Thinning via Multi-Subspace Tracking

机译：通过多子空间跟踪在线减薄

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摘要

In an era of ubiquitous large-scale streaming data, the availability of data far exceeds the capacity of expert human analysts. In many settings, such data is either discarded or stored unprocessed in data centers. This paper proposes a method of online data thinning, in which large-scale streaming datasets are winnowed to preserve unique, anomalous, or salient elements for timely expert analysis. At the heart of this proposed approach is an online anomaly detection method based on dynamic, low-rank Gaussian mixture models. Specifically, the high-dimensional covariance matrices associated with the Gaussian components are associated with low-rank models. According to this model, most observations lie near a union of subspaces. The low-rank modeling mitigates the curse of dimensionality associated with anomaly detection for high-dimensional data, and recent advances in subspace clustering and subspace tracking allow the proposed method to adapt to dynamic environments. Furthermore, the proposed method allows subsampling, is robust to missing data, and uses a mini-batch online optimization approach. The resulting algorithms are scalable, efficient, and are capable of operating in real time. Experiments on wide-area motion imagery and e-mail databases illustrate the efficacy of the proposed approach.
机译：在普遍存在的大规模流数据的时代，数据的可用性远远超过专家分析师的能力。在许多设置中，这些数据要么丢弃或在数据中心中未加工。本文提出了一种在线数据变薄的方法，其中需要大规模流数据集以保持独特，异常或突出的元素以及时的专家分析。在这种方法的核心，是一种基于动态，低级高斯混合模型的在线异常检测方法。具体地，与高斯组件相关联的高维协方差矩阵与低秩模型相关联。根据这一模型，大多数观测靠近子空间的联盟。低级模型减轻了与高维数据的异常检测相关的维度诅咒，并且子空间聚类和子空间跟踪中的最新进步允许提出的方法适应动态环境。此外，所提出的方法允许对数据采样，缺少数据是强大的，并使用迷你批处理在线优化方法。得到的算法是可扩展，高效的，并且能够实时操作。广域运动图像和电子邮件数据库的实验说明了所提出的方法的功效。

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