In this paper, we propose a latent multi-task learning algorithm to solve the multi-device indoor localization problem. Traditional indoor localization systems often assume that the collected signal data distributions are fixed, and thus the localization model learned on one device can be used on other devices without adaptation. However, by empirically studying the signal variation over different devices, we found this assumption to be invalid in practice. To solve this problem, we treat multiple devices as multiple learning tasks, and propose a multi-task learning algorithm. Different from algorithms assuming that the hypotheses learned from the original data space for related tasks can be similar, we only require the hypotheses learned in a latent feature space are similar. To establish our algorithm, we employ an alternating optimization approach to iteratively learn feature mappings and multi-task regression models for the devices. We apply our latent multi-task learning algorithm to real-world indoor localization data and demonstrate its effectiveness.
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