Dimension reduction in radio maps based on the supervised kernel principal component analysis

Abstract

Differently from most existing studies either directly eliminating redundant WiFi APs with trivial importance or adopting unsupervised dimension reduction methods, e.g. principal component analysis (PCA), this paper employs a supervised approach to take the full advantage of the information available for building radio maps, i.e. location labels attached to fingerprints, to compress original radio maps. Specifically, in the offline phase, the supervised kernel PCA (SKPCA) method is employed to derive a nonlinear and optimal embedding in a low-dimensional subspace; in the online phase, any sample vector containing received signal strengths can be projected onto the optimal subspace in real-time for further localization processing. Experiments are carried out not only in a real environment but also using an open dataset. It is shown that the compressed radio maps based on SKPCA have much smaller sizes than their original radio maps, but achieve similar localization performance and significantly outperform the other two popular PCA- based unsupervised dimension reduction methods, i.e. PCA and PCA-MLE.