Dimensionality reduction of radio map with nonlinear autoencoder

Presented is a nonlinear autoencoder, which reduces the dimensionality of a radio map, and consequently contributes to the reduction of the power consumption of client devices in a fingerprint-based indoor localisation. The nonlinear encoder shows significantly better performance in reducing the dimensionality than principal component analysis, a representative linear technique, did.