  With the recent development in mobile computing devices and as the ubiquitous
deployment of access points(APs) of Wireless Local Area Networks(WLANs), WLAN
based indoor localization systems(WILSs) are of mounting concentration and are
becoming more and more prevalent for they do not require additional
infrastructure. As to the localization methods in WILSs, for the approaches
used to localization in satellite based global position systems are difficult
to achieve in indoor environments, fingerprint based localization
algorithms(FLAs) are predominant in the RSS based schemes. However, the
performance of FLAs has close relationship with the number of APs and the
number of reference points(RPs) in WILSs, especially as the redundant
deployment of APs and RPs in the system. There are two fatal problems, curse of
dimensionality (CoD) and asymmetric matching(AM), caused by increasing number
of APs and breaking down APs during online stage. In this paper, a
semi-supervised RSS dimensionality reduction algorithm is proposed to solve
these two dilemmas at the same time and there are numerous analyses about the
theoretical realization of the proposed method. Another significant innovation
of this paper is jointing the fingerprint based algorithm with CM-SDE algorithm
to improve the localization accuracy of indoor localization.
