Heterogeneous transfer learning for activity recognition using heuristic search techniques

Purpose – The purpose of this paper is to study heterogeneous transfer learning for activity recognition using heuristic search techniques. Many pervasive computing applications require information about the activities currently being performed, but activity recognition algorithms typically require substantial amounts of labeled training data for each setting. One solution to this problem is to leverage transfer learning techniques to reuse available labeled data in new situations. Design/methodology/approach – This paper introduces three novel heterogeneous transfer learning techniques that reverse the typical transfer model and map the target feature space to the source feature space and apply them to activity recognition in a smart apartment. This paper evaluates the techniques on data from 18 different smart apartments located in an assisted-care facility and compares the results against several baselines. Findings – The three transfer learning techniques are all able to outperform the baseline compar...

[1]  D. Cook,et al.  Author's Personal Copy Pervasive and Mobile Computing Activity Knowledge Transfer in Smart Environments , 2022 .

[2]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[3]  J. P. Byrnes,et al.  Cognitive Development and Learning in Instructional Contexts , 1995 .

[4]  Shao-zi Li,et al.  Transfer AdaBoost learning for action recognition , 2009, 2009 IEEE International Symposium on IT in Medicine & Education.

[5]  Sebastian Thrun,et al.  Explanation-based neural network learning a lifelong learning approach , 1995 .

[6]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[7]  Ramesh Nallapati,et al.  A Comparative Study of Methods for Transductive Transfer Learning , 2007, Seventh IEEE International Conference on Data Mining Workshops (ICDMW 2007).

[8]  Sebastian Thrun,et al.  Learning to Learn , 1998, Springer US.

[9]  David E. Goldberg,et al.  Genetic algorithms and Machine Learning , 1988, Machine Learning.

[10]  E. Thorndike,et al.  The influence of improvement in one mental function upon the efficiency of other functions. II. The estimation of magnitudes. , 1901 .

[11]  Yi Yao,et al.  Boosting for transfer learning with multiple sources , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[12]  G. Englebienne,et al.  Transferring Knowledge of Activity Recognition across Sensor Networks , 2010, Pervasive.

[13]  Thomas G. Dietterich.  Multiple Classifier Systems , 2000, Lecture Notes in Computer Science.

[14]  Daniel Marcu,et al.  Domain Adaptation for Statistical Classifiers , 2006, J. Artif. Intell. Res..

[15]  Qiang Yang,et al.  Transfer Learning for Text Mining , 2012, Mining Text Data.

[16]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[17]  Diane J. Cook,et al.  Multi Home Transfer Learning for Resident Activity Discovery and Recognition , 2010 .

[18]  Gwenn Englebienne,et al.  Recognizing Activities in Multiple Contexts using Transfer Learning , 2008, AAAI Fall Symposium: AI in Eldercare: New Solutions to Old Problems.

[19]  Susan M. Barnett,et al.  When and where do we apply what we learn? A taxonomy for far transfer. , 2002, Psychological bulletin.

[20]  Qiang Yang,et al.  Translated Learning: Transfer Learning across Different Feature Spaces , 2008, NIPS.

[21]  David H. Wolpert,et al.  Stacked generalization , 1992, Neural Networks.

[22]  Melanie Mitchell,et al.  An introduction to genetic algorithms , 1996 .

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

[24]  Qiang Yang,et al.  Self-taught clustering , 2008, ICML '08.

[25]  Changshui Zhang,et al.  Transferred Dimensionality Reduction , 2008, ECML/PKDD.

[26]  D FeuzKyle,et al.  Transfer Learning in Activity Recognition , 2013 .

[27]  Charles Elkan,et al.  The Foundations of Cost-Sensitive Learning , 2001, IJCAI.

[28]  Benno Stein,et al.  Cross-Lingual Adaptation Using Structural Correspondence Learning , 2010, TIST.

[29]  John Blitzer,et al.  Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification , 2007, ACL.

[30]  Lars Kai Hansen,et al.  Neural Network Ensembles , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[31]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[32]  Ricardo Vilalta,et al.  A Perspective View and Survey of Meta-Learning , 2002, Artificial Intelligence Review.

[33]  Diane J. Cook,et al.  Transfer learning for activity recognition: a survey , 2013, Knowledge and Information Systems.