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We present a novel approach to indoor wireless localization using label propagation based on semi-supervised learning. Our aim is to reduce the effort of collecting labeled data in the offline training phrase, which are expensive to obtain. This learning algorithm combines labeled and unlabeled data in learning process to fully realize a global consistency assumption: similar data should have similar labels, which has intimate connections with random walks to propagate label through the dataset along high density areas defined by unlabeled data. We test our algorithm in 802.11 wireless LAN environments, and demonstrate the advantage of our approach in both accuracy and its ability to utilize a much smaller set of labeled training data.