Indoor localization via -graph regularized semi-supervised manifold learning

In this paper,a new l1-graph regularized semi-supervised manifold learning(LRSML) method is proposed for indoor localization.Due to noise corruption and non-linearity of received signal strength(RSS),traditional approaches always fail to deliver accurate positioning results.The l1-graph is constructed by sparse representation of each sample with respect to remaining samples.Noise factor is considered in the construction process of l1-graph,leading to more robustness compared to traditional k-nearest-neighbor graph(KNN-graph).The KNN-graph construction is supervised,while the l1-graph is assumed to be unsupervised without harnessing any data label information and uncovers the underlying sparse relationship of each data.Combining KNN-graph and l1-graph,both labeled and unlabeled information are utilized,so the LRSML method has the potential to convey more discriminative information compared to conventional methods.To overcome the non-linearity of RSS,kernel-based manifold learning method(K-LRSML) is employed through mapping the original signal data to a higher dimension Hilbert space.The efficiency and superiority of LRSML over current state of art methods are verified with extensive experiments on real data.