改进固定距离邻居搜索，加权自适应半径提升室内定位精度
(Weighted) Adaptive Radius Near Neighbor Search: Evaluation for WiFi Fingerprint-based Positioning
- 用自适应半径替代固定距离，动态调整搜索范围
- 加权自适应版本在22个数据集上表现优于多数kNN变体
- 适合需要高精度的无线定位场景，如室内导航
固定半径近邻（FRNN）是广泛使用的k近邻（kNN）的替代方法。与kNN不同，FRNN根据预设距离内的所有训练样本对测试样本进行标签或估计。然而，对所有训练样本采用固定最大距离会降低FRNN的准确性。因此，本文提出自适应半径近邻（ARNN）和加权自适应近邻（WARNN），分别引入自适应距离和权重。三种方法与kNN及其12种变体在22个不同数据集上对比，用于回归任务——基于WiFi指纹的室内定位。尽管测试的FRNN和ARNN版本表现较弱，但四个最佳方法中有三个为WARNN版本，表明结合权重与自适应距离可达到甚至超越kNN变体的性能。
原文摘要 · Abstract (English)
Fixed Radius Near Neighbor (FRNN) search is an alternative to the widely used k Nearest Neighbors (kNN) search. Unlike kNN, FRNN determines a label or an estimate for a test sample based on all training samples within a predefined distance. While this approach is beneficial in certain scenarios, assuming a fixed maximum distance for all training samples can decrease the accuracy of the FRNN. Therefore, in this paper we propose the Adaptive Radius Near Neighbor (ARNN) and the Weighted ARNN (WARNN), which employ adaptive distances and in latter case weights. All three methods are compared to kNN and twelve of its variants for a regression problem, namely WiFi fingerprinting indoor positioning, using 22 different datasets to provide a comprehensive analysis. While the performances of the tested FRNN and ARNN versions were amongst the worse, three of the four best methods in the test were WARNN versions, indicating that using weights together with adaptive distances achieves performance comparable or even better than kNN variants.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。