  The main limitation that constrains the fast and comprehensive application of
Wireless Local Area Network (WLAN) based indoor localization systems with
Received Signal Strength (RSS) positioning algorithms is the building of the
fingerprinting radio map, which is time-consuming especially when the indoor
environment is large and/or with high frequent changes. Different approaches
have been proposed to reduce workload, including fingerprinting deployment and
update efforts, but the performance degrades greatly when the workload is
reduced below a certain level. In this paper, we propose an indoor localization
scenario that applies metric learning and manifold alignment to realize direct
mapping localization (DML) using a low resolution radio map with single sample
of RSS that reduces the fingerprinting workload by up to 87\%. Compared to
previous work. The proposed two localization approaches, DML and $k$ nearest
neighbors based on reconstructed radio map (reKNN), were shown to achieve less
than 4.3\ m and 3.7\ m mean localization error respectively in a typical office
environment with an area of approximately 170\ m$^2$, while the unsupervised
localization with perturbation algorithm was shown to achieve 4.7\ m mean
localization error with 8 times more workload than the proposed methods. As for
the room level localization application, both DML and reKNN can meet the
requirement with at most 9\ m of localization error which is enough to tell
apart different rooms with over 99\% accuracy.
