Multi-instance graphical transfer clustering for traffic data learning

Shan Xue, Jie Lu, Jia Wu, Guangquan Zhang, Li Xiong

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

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 languageEnglish
Title of host publicationIJCNN 2016
Subtitle of host publicationProceedings of the 2016 International Joint Conference on Neural Networks
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages4390-4395
Number of pages6
ISBN (Electronic)9781509006205, 9781509006199
ISBN (Print)9781509006212
DOIs
Publication statusPublished - 31 Oct 2016
Externally publishedYes
Event2016 International Joint Conference on Neural Networks, IJCNN 2016 - Vancouver, Canada
Duration: 24 Jul 201629 Jul 2016

Conference

Conference2016 International Joint Conference on Neural Networks, IJCNN 2016
CountryCanada
CityVancouver
Period24/07/1629/07/16

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