Neighbor Oblivious Learning (NObLe) for Device Localization and Tracking

On-device localization and tracking are increasingly crucial for various applications. Machine learning (ML) techniques are widely adopted along with the rapidly growing amount of data. However, during training, almost none of ML techniques incorporate the known structural information such as floor plan, which can be especially useful in indoor or other structured environments. The problem is incredibly hard because the structural properties are not explicitly available, making most structural learning approaches inapplicable. We study our method through the intuitions from manifold learning. Whereas existing manifold methods utilizes neighborhood information such as Euclidean distances, we quantize the output space to measure closeness on the structure. We propose Neighbor Oblivious Learning (NObLe) and demonstrate our approach's effectiveness on two applications, Wi-Fi-based fingerprint localization and inertial measurement unit(IMU) based device tracking. We show that NObLe gives significant improvement over state-of-art prediction accuracy.