Transfer learning for WiFi-based indoor localization

The WiFi-based indoor localization problem (WILP) aims to detect the location of a client device given the signals received from various access points. WILP is a complex and very important task for many AI and ubiquitous computing applications. A major approach to solving this task is through machine learning, where upto-date labeled training data are required in a large scale indoor environment. In this paper, we identify WILP as a transfer learning problem, because the WiFi data are highly dependent on contextual changes. We show that WILP can be modeled as a transfer learning problem for regression modeling, where we identify several important cases of knowledge transfer that range from transferring the localization models over time, across space and across client devices. We also share our working experience in WILP and transfer learning research in a realistic problem solving setting, and discuss a data set we have made public for advancing this research.

[1]  Dieter Fox,et al.  Gaussian Processes for Signal Strength-Based Location Estimation , 2006, Robotics: Science and Systems.

[2]  Bruno Sinopoli,et al.  A kernel-based learning approach to ad hoc sensor network localization , 2005, TOSN.

[3]  Qiang Yang,et al.  CIGAR: Concurrent and Interleaving Goal and Activity Recognition , 2008 .

[4]  Colin L. Mallows,et al.  A system for LEASE: location estimation assisted by stationary emitters for indoor RF wireless networks , 2004, IEEE INFOCOM 2004.

[5]  Qiang Yang,et al.  Co-clustering based classification for out-of-domain documents , 2007, KDD '07.

[6]  Massimiliano Pontil,et al.  Multi-Task Feature Learning , 2006, NIPS.

[7]  V. Padmanabhan,et al.  Enhancements to the RADAR User Location and Tracking System , 2000 .

[8]  John Blitzer,et al.  Domain Adaptation with Structural Correspondence Learning , 2006, EMNLP.

[9]  Qiang Yang,et al.  Adaptive Localization in a Dynamic WiFi Environment through Multi-view Learning , 2007, AAAI.

[10]  Mikhail Belkin,et al.  Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples , 2006, J. Mach. Learn. Res..

[11]  Qiang Yang,et al.  Transferring Localization Models across Space , 2008, AAAI.

[12]  Qiang Yang,et al.  Adaptive Temporal Radio Maps for Indoor Location Estimation , 2005, Third IEEE International Conference on Pervasive Computing and Communications.

[13]  Rich Caruana,et al.  Multitask Learning , 1997, Machine-mediated learning.

[14]  Ashwin Ram,et al.  Transfer Learning in Real-Time Strategy Games Using Hybrid CBR/RL , 2007, IJCAI.

[15]  Qiang Yang,et al.  Transferring Multi-device Localization Models using Latent Multi-task Learning , 2008, AAAI.

[16]  Motoaki Kawanabe,et al.  Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation , 2007, NIPS.

[17]  Massimiliano Pontil,et al.  Convex multi-task feature learning , 2008, Machine Learning.

[18]  Rajat Raina,et al.  Self-taught learning: transfer learning from unlabeled data , 2007, ICML '07.

[19]  Rajat Raina,et al.  Constructing informative priors using transfer learning , 2006, ICML.

[20]  Neil D. Lawrence,et al.  WiFi-SLAM Using Gaussian Process Latent Variable Models , 2007, IJCAI.

[21]  Yiqiang Chen,et al.  Accurate and Low-cost Location Estimation Using Kernels , 2005, IJCAI.

[22]  Dieter Fox,et al.  Large-Scale Localization from Wireless Signal Strength , 2005, AAAI.

[23]  Qiang Yang,et al.  A Manifold Regularization Approach to Calibration Reduction for Sensor-Network Based Tracking , 2006, AAAI.

[24]  Bernhard Schölkopf,et al.  Correcting Sample Selection Bias by Unlabeled Data , 2006, NIPS.

[25]  Qiang Yang,et al.  Transfer Learning via Dimensionality Reduction , 2008, AAAI.

[26]  Qiang Yang,et al.  Estimating Location Using Wi-Fi , 2008, IEEE Intelligent Systems.

[27]  Qiang Yang,et al.  Boosting for transfer learning , 2007, ICML '07.

[28]  Qiang Yang,et al.  Transferring Localization Models over Time , 2008, AAAI.

[29]  Tong Zhang,et al.  A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data , 2005, J. Mach. Learn. Res..