Distribution-based semi-supervised learning for activity recognition
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 cr...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2019
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/106004 http://hdl.handle.net/10220/49629 |
| _version_ | 1826126090240786432 |
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| author | Qian, Hangwei Pan, Sinno Jialin Miao, Chunyan |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Qian, Hangwei Pan, Sinno Jialin Miao, Chunyan |
| author_sort | Qian, Hangwei |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T06:47:02Z |
| format | Conference Paper |
| id | ntu-10356/106004 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:47:02Z |
| publishDate | 2019 |
| record_format | dspace |
| spelling | ntu-10356/1060042022-07-21T08:19:29Z Distribution-based semi-supervised learning for activity recognition Qian, Hangwei Pan, Sinno Jialin Miao, Chunyan School of Computer Science and Engineering Interdisciplinary Graduate School (IGS) The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19) Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY) Wireless-sensor-based Engineering::Computer science and engineering Semi-supervised Learning 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. 2019-08-14T06:46:29Z 2019-12-06T22:02:42Z 2019-08-14T06:46:29Z 2019-12-06T22:02:42Z 2019 Conference Paper 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). https://hdl.handle.net/10356/106004 http://hdl.handle.net/10220/49629 en © 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). 8 p. application/pdf |
| spellingShingle | Wireless-sensor-based Engineering::Computer science and engineering Semi-supervised Learning Qian, Hangwei Pan, Sinno Jialin Miao, Chunyan Distribution-based semi-supervised learning for activity recognition |
| title | Distribution-based semi-supervised learning for activity recognition |
| title_full | Distribution-based semi-supervised learning for activity recognition |
| title_fullStr | Distribution-based semi-supervised learning for activity recognition |
| title_full_unstemmed | Distribution-based semi-supervised learning for activity recognition |
| title_short | Distribution-based semi-supervised learning for activity recognition |
| title_sort | distribution based semi supervised learning for activity recognition |
| topic | Wireless-sensor-based Engineering::Computer science and engineering Semi-supervised Learning |
| url | https://hdl.handle.net/10356/106004 http://hdl.handle.net/10220/49629 |
| work_keys_str_mv | AT qianhangwei distributionbasedsemisupervisedlearningforactivityrecognition AT pansinnojialin distributionbasedsemisupervisedlearningforactivityrecognition AT miaochunyan distributionbasedsemisupervisedlearningforactivityrecognition |