A probabilistic model with parsinomious representation for sensor fusion in recognizing activity in pervasive environment
To tackle the problem of increasing numbers of state transition parameters when the number of sensors increases, we present a probabilistic model together with several parsinomious representations for sensor fusion. These include context specific independence (CSI), mixtures of smaller multinomials...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
IEEE Coputer Society Conference Publishing Services
2006
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| Online Access: | http://hdl.handle.net/20.500.11937/10676 |
| _version_ | 1848747598496661504 |
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| author | Tran, Dung Phung, Dinh Bui, H.H. Venkatesh, Svetha |
| author2 | Y.Y. Tang |
| author_facet | Y.Y. Tang Tran, Dung Phung, Dinh Bui, H.H. Venkatesh, Svetha |
| author_sort | Tran, Dung |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | To tackle the problem of increasing numbers of state transition parameters when the number of sensors increases, we present a probabilistic model together with several parsinomious representations for sensor fusion. These include context specific independence (CSI), mixtures of smaller multinomials and softmax function representations to compactly represent the state transitions of a large number of sensors. The model is evaluated on real-world data acquired through ubiquitous sensors in recognizing daily morning activities. The results show that the combination of CSI and mixtures of smaller multinomials achieves comparable performance with much fewer parameters. |
| first_indexed | 2025-11-14T06:51:42Z |
| format | Conference Paper |
| id | curtin-20.500.11937-10676 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:51:42Z |
| publishDate | 2006 |
| publisher | IEEE Coputer Society Conference Publishing Services |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-106762022-10-20T07:09:56Z A probabilistic model with parsinomious representation for sensor fusion in recognizing activity in pervasive environment Tran, Dung Phung, Dinh Bui, H.H. Venkatesh, Svetha Y.Y. Tang S.P.Wang G. Lorette D.S. Young H. Yang To tackle the problem of increasing numbers of state transition parameters when the number of sensors increases, we present a probabilistic model together with several parsinomious representations for sensor fusion. These include context specific independence (CSI), mixtures of smaller multinomials and softmax function representations to compactly represent the state transitions of a large number of sensors. The model is evaluated on real-world data acquired through ubiquitous sensors in recognizing daily morning activities. The results show that the combination of CSI and mixtures of smaller multinomials achieves comparable performance with much fewer parameters. 2006 Conference Paper http://hdl.handle.net/20.500.11937/10676 10.1109/ICPR.2006.154 IEEE Coputer Society Conference Publishing Services restricted |
| spellingShingle | Tran, Dung Phung, Dinh Bui, H.H. Venkatesh, Svetha A probabilistic model with parsinomious representation for sensor fusion in recognizing activity in pervasive environment |
| title | A probabilistic model with parsinomious representation for sensor fusion in recognizing activity in pervasive environment |
| title_full | A probabilistic model with parsinomious representation for sensor fusion in recognizing activity in pervasive environment |
| title_fullStr | A probabilistic model with parsinomious representation for sensor fusion in recognizing activity in pervasive environment |
| title_full_unstemmed | A probabilistic model with parsinomious representation for sensor fusion in recognizing activity in pervasive environment |
| title_short | A probabilistic model with parsinomious representation for sensor fusion in recognizing activity in pervasive environment |
| title_sort | probabilistic model with parsinomious representation for sensor fusion in recognizing activity in pervasive environment |
| url | http://hdl.handle.net/20.500.11937/10676 |