Abstract
In order to better model complex real-world data and to develop robust features that capture relevant information, we usually employ unsupervised feature learning to learn a layer of features representations from unlabeled data. However, developing domain-specific features for each task is expensive, time-consuming and requires expertise of the data. In this paper, we introduce multi-instance clustering and graphical learning to unsupervised transfer learning. For a better clustering efficient, we proposed a set of algorithms on the application of traffic data learning, instance feature representation, distance calculation of multi-instance clustering, multi-instance graphical cluster initialisation, multi-instance multi-cluster update, and graphical multi-instance transfer clustering (GMITC). In the end of this paper, we examine the proposed algorithms on the Eastwest datasets by couples of baselines. The experiment results indicate that our proposed algorithms can get higher clustering accuracy and much higher programming speed.
Original language | English |
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Title of host publication | IJCNN 2016 |
Subtitle of host publication | Proceedings of the 2016 International Joint Conference on Neural Networks |
Place of Publication | Piscataway, NJ |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 4390-4395 |
Number of pages | 6 |
ISBN (Electronic) | 9781509006205, 9781509006199 |
ISBN (Print) | 9781509006212 |
DOIs | |
Publication status | Published - 31 Oct 2016 |
Externally published | Yes |
Event | 2016 International Joint Conference on Neural Networks, IJCNN 2016 - Vancouver, Canada Duration: 24 Jul 2016 → 29 Jul 2016 |
Conference
Conference | 2016 International Joint Conference on Neural Networks, IJCNN 2016 |
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Country/Territory | Canada |
City | Vancouver |
Period | 24/07/16 → 29/07/16 |