具有切换代价的多序列异常搜索
Anomaly Search Over Many Sequences With Switching Costs
摘要：
本文考虑了在大量数据流中识别异常的最快搜索问题。例如，这些流可以对移动机器人监视的不相交区域进行建模。一个特殊的挑战是问题的一个版本，其中实验者必须承受每次被采样的数据流发生变化时的成本，例如机器人必须花费在区域之间移动的时间。在本文中，我们提出了一种算法，该算法占切换成本通过改变置信度阈值，当该算法切换到一个新的数据流，管理。我们的主要贡献是很容易计算的近似值的最优值的阈值和最优值的参数，确定何时流必须重新采样。此外，我们经验表明（i）一个统一的改善感兴趣的切换成本和（ii）大致相当的性能，小切换成本相比，最接近的可用算法。
英文摘要：
This paper considers the quickest search problem to identify anomalies among large numbers of data streams. These streams can model, for example, disjoint regions monitored by a mobile robot. A particular challenge is a version of the problem in which the experimenter must suffer a cost each time the data stream being sampled changes, such as the time the robot must spend moving between regions. In this paper, we propose an algorithm which accounts for switching costs by varying a confidence threshold that governs when the algorithm switches to a new data stream. Our main contributions are easily computable approximations for both the optimal value of this threshold and the optimal value of the parameter that determines when a stream must be re-sampled. Further, we empirically show (i) a uniform improvement for switching costs of interest and (ii) roughly equivalent performance for small switching costs when comparing to the closest available algorithm.
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