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Distribution-based Semi-Supervised Learning for Activity Recognition


Authors: H. Qian, S. J. Pan, and C. Miao
Title: Distribution-based Semi-Supervised Learning for Activity Recognition
Abstract: Supervised learning methods have been widely applied to activity recognition. The prevalent success of existing methods, however, has two crucial prerequisites: proper feature extraction and sufficient labeled training data. The former is important to differentiate activities, while the latter is crucial to build a precise learning model. These two prerequisites have become bottlenecks to make existing methods more practical. Most existing feature extraction methods highly depend on domain knowledge, while labeled data requires intensive human annotation effort. Therefore, in this paper, we propose a novel method, named Distribution-based Semi-Supervised Learning, to tackle the aforementioned limitations. The proposed method is capable of automatically extracting powerful features with no domain knowledge required, meanwhile, alleviating the heavy annotation effort through semi-supervised learning. Specifically, we treat data stream of sensor readings received in a period as a distribution, and map all training distributions, including labeled and unlabeled, into a reproducing kernel Hilbert space (RKHS) using the kernel mean embedding technique. The RKHS is further altered by exploiting the underlying geometry structure of the unlabeled distributions. Finally, in the altered RKHS, a classifier is trained with the labeled distributions. We conduct extensive experiments on three public datasets to verify the effectiveness of our method compared with state-of-the-art baselines.
Keywords: 
Conference Name: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence
Location: Honolulu, Hawaii, USA
Publisher: AAAI
Year: 2019
Accepted PDF File: Distribution-based_Semi-Supervised_Learning_for_Activity_Recognition_accepted.pdf
Permanent Link: https://doi.org/10.1609/aaai.v33i01.33017699
Reference: H. Qian, S. J. Pan, and C. Miao, "Distribution-based Semi-Supervised Learning for Activity Recognition," in Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, Honolulu, Hawaii, USA, January-February 2019, vol. 33, no. 01, pp. 7699-7706: AAAI
bibtex: 
@inproceedings{LILY-c167, 
   author	= {Qian, Hangwei and Pan, Sinno Jialin and Miao, Chunyan},
   title		= {Distribution-based Semi-Supervised Learning for Activity Recognition},  
   booktitle	= {Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence}, 
   year		= {2019}, 
   month	= {January--Feburary}, 
   pages	= {7699-7706}, 
   location	= {Honolulu, Hawaii, USA},
   publisher	= {AAAI},
}