Feature Relevance Network-Based Transfer Learning for Indoor Location Estimation

We present a new machine learning framework for indoor location estimation. In many cases, locations could be easily estimated using various traditional positioning methods and conventional machine learning approaches based on signalling devices, e.g., access points (APs). When there exist environmental changes, however, such traditional methods cannot be employed due to data distribution change. In order to circumvent this difficulty, we introduce feature relevance network-based method, which focuses on interrelatedness among features. Feature relevance networks are connected graphs representing concurrency of the signalling devices such as APs. In the newly created relevance network, a test instance and the prototype of a location are expanded until convergence. The expansion cost corresponds to distance between the test instance and the prototype. Unlike other methods, our model is nonparametric making no assumptions about signal distributions. The proposed method is applied to the 2007 IEEE International Conference on Data Mining Data Mining Contest Task #2 (transfer learning), which is a typical example situation where the training and test datasets have been gathered during different periods. Using the proposed method, we accomplish the estimation accuracy of 0.3238, which is better than the best result of the contest.