  Recognizing user location with WiFi fingerprints is a popular approach for
accurate indoor positioning problems. In this work, our goal is to interpret
WiFi fingerprints into actual user locations. However, WiFi fingerprint data
can be very high dimensional in some cases, we need to find a good
representation of the input data for the learning task first. Otherwise, using
neural networks will suffer from severe overfitting. In this work, we solve
this issue by combining the Information Bottleneck method and Variational
Inference. Based on these two approaches, we propose a Variational Information
Bottleneck model for accurate indoor positioning. The proposed model consists
of an encoder structure and a predictor structure. The encoder is to find a
good representation in the input data for the learning task. The predictor is
to use the latent representation to predict the final output. To enhance the
generalization of our model, we also adopt the Dropout technique for each
hidden layer of the decoder. We conduct the validation experiments on a
real-world dataset. We also compare the proposed model to other existing
methods so as to quantify the performances of our method.
