Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/106004
Title: Distribution-based semi-supervised learning for activity recognition
Authors: Qian, Hangwei
Pan, Sinno Jialin
Miao, Chunyan
Keywords: Wireless-sensor-based
Engineering::Computer science and engineering
Semi-supervised Learning
Issue Date: 2019
Source: Qian, H., Pan, S. J., & Miao, C. (2019). Distribution-based semi-supervised learning for activity recognition. The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19).
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.
URI: https://hdl.handle.net/10356/106004
http://hdl.handle.net/10220/49629
Rights: © 2019 Association for the Advancement of Artificial Intelligence (AAAI). All rights reserved. This paper was published in The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19) and is made available with permission of Association for the Advancement of Artificial Intelligence (AAAI).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:IGS Conference Papers
SCSE Conference Papers

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