Quantile Search with Time-Varying Search Parameter
John J. Lipor, Gautam Dasarathy · 2018 52nd Asilomar Conference on Signals, Systems, and Computers · 2018
We consider the problem of active learning in the context of spatial sampling, where the sampling cost is a function of both the number of samples taken and the distance traveled during the sampling procedure. We present Uniform-to-Binary (UTB) search, a novel algorithm in this setting. UTB search extends the Quantile Search (QS) algorithm [1] such that the tuning parameter m is allowed to vary throughout the search procedure. We analyze the algorithm in terms of both sample complexity and distance traveled. Empirical results show that our proposed method outperforms QS with fixed m in all cases considered.