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Conference Paper: Cold-start heterogeneous-device wireless localization
Title | Cold-start heterogeneous-device wireless localization |
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Authors | |
Issue Date | 2016 |
Citation | 30th AAAI Conference on Artificial Intelligence, AAAI 2016, 2016, p. 1429-1435 How to Cite? |
Abstract | In this paper, we study a cold-start heterogeneous-device localization problem. This problem is challenging, because it results in an extreme inductive transfer learning setting, where there is only source domain data but no target domain data. This problem is also underexplored. As there is no target domain data for calibration, we aim to learn a robust feature representation only from the source domain. There is little previous work on such a robust feature learning task; besides, the existing robust feature representation proposals are both heuristic and inexpressive. As our contribution, we for the first time provide a principled and expressive robust feature representation to solve the challenging cold-start heterogeneous-device localization problem. We evaluate our model on two public real-world data sets, and show that it significantly outperforms the best baseline by 23.1%-91.3% across four pairs of heterogeneous devices. |
Persistent Identifier | http://hdl.handle.net/10722/345221 |
DC Field | Value | Language |
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dc.contributor.author | Zheng, Vincent W. | - |
dc.contributor.author | Cao, Hong | - |
dc.contributor.author | Gao, Shenghua | - |
dc.contributor.author | Adhikari, Aditi | - |
dc.contributor.author | Lin, Miao | - |
dc.contributor.author | Chang, Kevin Chen Chuan | - |
dc.date.accessioned | 2024-08-15T09:25:59Z | - |
dc.date.available | 2024-08-15T09:25:59Z | - |
dc.date.issued | 2016 | - |
dc.identifier.citation | 30th AAAI Conference on Artificial Intelligence, AAAI 2016, 2016, p. 1429-1435 | - |
dc.identifier.uri | http://hdl.handle.net/10722/345221 | - |
dc.description.abstract | In this paper, we study a cold-start heterogeneous-device localization problem. This problem is challenging, because it results in an extreme inductive transfer learning setting, where there is only source domain data but no target domain data. This problem is also underexplored. As there is no target domain data for calibration, we aim to learn a robust feature representation only from the source domain. There is little previous work on such a robust feature learning task; besides, the existing robust feature representation proposals are both heuristic and inexpressive. As our contribution, we for the first time provide a principled and expressive robust feature representation to solve the challenging cold-start heterogeneous-device localization problem. We evaluate our model on two public real-world data sets, and show that it significantly outperforms the best baseline by 23.1%-91.3% across four pairs of heterogeneous devices. | - |
dc.language | eng | - |
dc.relation.ispartof | 30th AAAI Conference on Artificial Intelligence, AAAI 2016 | - |
dc.title | Cold-start heterogeneous-device wireless localization | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.scopus | eid_2-s2.0-84991437253 | - |
dc.identifier.spage | 1429 | - |
dc.identifier.epage | 1435 | - |