Voronoi diagrams—a survey of a fundamental geometric data structure
ACM Computing Surveys (CSUR)
The Journal of Machine Learning Research
Clustering on the Unit Hypersphere using von Mises-Fisher Distributions
The Journal of Machine Learning Research
A probabilistic approach to spatiotemporal theme pattern mining on weblogs
Proceedings of the 15th international conference on World Wide Web
Mining geographic knowledge using location aware topic model
Proceedings of the 4th ACM workshop on Geographical information retrieval
GeoFolk: latent spatial semantics in web 2.0 social media
Proceedings of the third ACM international conference on Web search and data mining
Distributed Algorithms for Topic Models
The Journal of Machine Learning Research
Geographical topic discovery and comparison
Proceedings of the 20th international conference on World wide web
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Discovering geographical topics in the twitter stream
Proceedings of the 21st international conference on World Wide Web
Hierarchical geographical modeling of user locations from social media posts
Proceedings of the 22nd international conference on World Wide Web
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Nowadays, large collections of photos are tagged with GPS coordinates. The modelling of such large geo-tagged corpora is an important problem in data mining and information retrieval, and involves the use of geographical information to detect topics with a spatial component. In this paper, we propose a novel geographical topic model which captures dependencies between geographical regions to support the detection of topics with complex, non-Gaussian distributed spatial structures. The model is based on a multi-Dirichlet process (MDP), a novel generalisation of the hierarchical Dirichlet process extended to support multiple base distributions. Our method thus is called the MDP-based geographical topic model (MGTM). We show how to use a MDP to dynamically smooth topic distributions between groups of spatially adjacent documents. In systematic quantitative and qualitative evaluations using independent datasets from prior related work, we show that such a model can exploit the adjacency of regions and leads to a significant improvement in the quality of topics compared to the state of the art in geographical topic modelling.