研究压缩数据在保真与异常识别间的权衡，提升系统整体性能。
RDD: Pareto Analysis of the Rate-Distortion-Distinguishability Trade-off
- 基于高斯假设构建三者权衡模型：码率、失真度、异常可区分性
- 提出帕累托前沿分析，优于传统仅优化码率-失真方案
- 适用于需高效传输且要检测异常的监控系统设计
大规模监控系统生成的数据通常需压缩以进行网络传输。压缩后的数据可能在云端用于异常检测等任务，但压缩带来的信息损失可能削弱检测器区分正常与异常模式的能力。本文扩展了文献[1]中的信息论框架，同时考虑系统有效性依赖的三个关键因素：压缩效率、引入的失真程度，以及压缩后正常信号与异常信号的可区分性。通过引入高斯假设，绘制出帕累托前沿曲线，表明沿该曲面调整参数可更优地管理三者之间的权衡，优于仅追求最优率-失真压缩并寄希望于信号仍可区分的传统方法。
原文摘要 · Abstract (English)
Extensive monitoring systems generate data that is usually compressed for network transmission. This compressed data might then be processed in the cloud for tasks such as anomaly detection. However, compression can potentially impair the detector's ability to distinguish between regular and irregular patterns due to information loss. Here we extend the information-theoretic framework introduced in [1] to simultaneously address the trade-off between the three features on which the effectiveness of the system depends: the effectiveness of compression, the amount of distortion it introduces, and the distinguishability between compressed normal signals and compressed anomalous signals. We leverage a Gaussian assumption to draw curves showing how moving on a Pareto surface helps administer such a trade-off better than simply relying on optimal rate-distortion compression and hoping that compressed signals can be distinguished from each other.
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