论文标题
基于不确定性的非参数活性峰检测
Uncertainty-Based Non-Parametric Active Peak Detection
论文作者
论文摘要
考虑活跃的非参数峰检测。作为用例，研究了主动源定位，并设计了一种基于不确定性的采样方案算法，以有效地从一些能量测量中定位峰值。结果表明，在非常温和的条件下，带有$ M $的源定位误差可主动选择的能量测量量表为$ O（\ log^2 m/m）$。从数值上讲，可以表明，在低样本制度中，所提出的方法在几种类型的数据上享有卓越的性能，并且优于最先进的被动源定位方法，而在低样本制度中，也可以胜过贪婪的方法。
Active, non-parametric peak detection is considered. As a use case, active source localization is examined and an uncertainty-based sampling scheme algorithm to effectively localize the peak from a few energy measurements is designed. It is shown that under very mild conditions, the source localization error with $m$ actively chosen energy measurements scales as $O(\log^2 m/m)$. Numerically, it is shown that in low-sample regimes, the proposed method enjoys superior performance on several types of data and outperforms the state-of-the-art passive source localization approaches and in the low sample regime, can outperform greedy methods as well.
