TY - GEN
T1 - Total variation regularization for training of indoor location fingerprints
AU - Tran, Duc A.
AU - Truong, Phong
PY - 2013
Y1 - 2013
N2 - Location fingerprinting is a common approach to indoor localization. For good accuracy, the training set of sample fingerprints, each mapping a fingerprint to a location, should be sufficiently large to be well-representative of the environment in terms of both spatial coverage and temporal coverage. Unfortunately, the task of collecting these samples can be tedious and labor-intensive because one must label each location that is being surveyed. On the other hand, fingerprints without location information are abundant and can easily be collected and so recent studies have tried to capitalize on these unlabeled fingerprints to improve the training set. The paper investigates how this goal can be achieved via graph regularization based on Total Variation (TV). TV is highly effective for semi-supervised learning in image processing but it is not clear whether its success can be transferred to indoor location fingerprinting.
AB - Location fingerprinting is a common approach to indoor localization. For good accuracy, the training set of sample fingerprints, each mapping a fingerprint to a location, should be sufficiently large to be well-representative of the environment in terms of both spatial coverage and temporal coverage. Unfortunately, the task of collecting these samples can be tedious and labor-intensive because one must label each location that is being surveyed. On the other hand, fingerprints without location information are abundant and can easily be collected and so recent studies have tried to capitalize on these unlabeled fingerprints to improve the training set. The paper investigates how this goal can be achieved via graph regularization based on Total Variation (TV). TV is highly effective for semi-supervised learning in image processing but it is not clear whether its success can be transferred to indoor location fingerprinting.
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U2 - 10.1145/2509338.2509346
DO - 10.1145/2509338.2509346
M3 - Conference contribution
AN - SCOPUS:84887216422
SN - 9781450323673
T3 - Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM
SP - 27
EP - 32
BT - MiSeNet 2013 - Proceedings of the 2nd ACM Annual International Workshop on Mission-Oriented Wireless Sensor Networking
T2 - 2nd ACM Annual International Workshop on Mission-Oriented Wireless Sensor Networking, MiSeNet 2013
Y2 - 4 October 2013 through 4 October 2013
ER -