USING SEMI-SUPERVISED VARIATIONAL AUTOENCODER FOR WI-FI-BASED INDOOR LOCALIZATION

Methods of training predictors for the location of a computing device in an indoor environment are provided. The methods comprise receiving training data comprising labelled data and unlabelled data. A method of training a predictor comprises training a variational autoencoder, wherein the variational autoencoder comprises encoder neural networks, which encode signal strength values in a latent variable, and decoder neural networks, which decode the latent variable to reconstructed signal strength values, and training a classification neural network that employs the latent variable to generate a predicted location. Another method of training a predictor comprises training a classification neural network together with a variational autoencoder, wherein the classification neural network receives signal strength values of the training data as input and outputs a predicted location to decoder neural networks of the variational autoencoder.