Unsupervised Activity Discovery and Characterization for Sensor-Rich Environments
This thesis presents an unsupervised method for discovering and analyzing the different kinds of activities in an active environment. Drawing from natural language processing, a novel representation of activities as bags of event n-grams is introduced, where the global structural information of acti...
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| Format: | Others |
| Language: | en_US |
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Georgia Institute of Technology
2007
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| Online Access: | http://hdl.handle.net/1853/14131 |