Transferring Multi-device Localization Models using Latent Multi-task Learning

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An Overview of Multi-task Learning (2018, background): “Applications of MTL in ubiquitous computing include stock prediction [126], multi-device localization [127], the inverse dynamics problem for robotics [128,129], estimation of travel costs on road networks [130], travel-time prediction on road networks [131], and traffic-sign recognition [132].”
Indoor localization in multi-floor environments with reduced effort (2010, background): “[17] and [18] addressed the environmental changes with time and devices in WiFi localization with minimal effort; however, both of them required at least some labeled data in the new environments (i.”
A Survey on Transfer Learning (2010, background): “In [75], [76], [77], [78], [79], transfer learning techniques are proposed to extract knowledge from WiFi localization models across time periods, space, and mobile devices, to benefit WiFi 1354 IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 22, NO. 10, OCTOBER 2010 7.”
Transfer Learning for Activity Recognition: A Survey (2013, methodology): “This mapping can be computed manually [66] or learned as part of the transfer learning algorithm [18,25,37,54,81].”