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However, most of the previous works in activity and sensor stream analysis have been focusing on one aspect of the data, e.g. only recognizing the type of the activity or only identifying the person who performed the activity. We instead propose an approach that uses a weakly supervised multi-output siamese network that learns to map the data into multiple representation spaces, where each representation space focuses on one aspect of the data. The representation vectors of the data samples are positioned in the space such that the data with the same semantic meaning in that aspect are closely located to each other. Therefore, as demonstrated with a set of experiments, the trained model can provide metrics for clustering data based on multiple aspects, allowing it to address multiple tasks simultaneously and even to outperform single task supervised methods in many situations. In addition, further experiments are presented that in more detail analyze the effect of the architecture and of using multiple tasks within this framework, that investigate the scalability of the model to include additional tasks, and that demonstrate the ability of the framework to combine data for which only partial relationship information with respect to the target tasks is available.<\/jats:p>","DOI":"10.1145\/3397330","type":"journal-article","created":{"date-parts":[[2020,6,15]],"date-time":"2020-06-15T22:30:37Z","timestamp":1592260237000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":25,"title":["Weakly Supervised Multi-Task Representation Learning for Human Activity Analysis Using Wearables"],"prefix":"10.1145","volume":"4","author":[{"given":"Taoran","family":"Sheng","sequence":"first","affiliation":[{"name":"The University of Texas at Arlington, Arlington, Texas, USA"}]},{"given":"Manfred","family":"Huber","sequence":"additional","affiliation":[{"name":"The University of Texas at Arlington, Arlington, Texas, USA"}]}],"member":"320","published-online":{"date-parts":[[2020,6,15]]},"reference":[{"key":"e_1_2_2_1_1","volume-title":"AAAI Conference on Artificial Intelligence","volume":"01","author":"Alsheikh Mohammad Abu","year":"2016","unstructured":"Mohammad Abu Alsheikh , Ahmed Selim , Dusit Niyato , Linda Doyle , Shaowei Lin , and Hwee Pink Tan . 2016 . 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