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Detecting Non-Gaussian Geographical Topics in Tagged Photo Collections
- 1. Institute for Web Science & Technologies University of Koblenz ▪ Landau, Germany Detecting Non-Gaussian Geographical Topics in Tagged Photo Collections Christoph Carl Kling, Jérôme Kunegis, Sergej Sizov, Steffen Staab
- 2. Detecting Non-Gaussian Geographical Topics 2Christoph Carl Kling Outline 1) Motivation 2) Existing approaches 3) Our approach 4) Evaluation
- 3. Detecting Non-Gaussian Geographical Topics 3Christoph Carl Kling Motivation
- 4. Detecting Non-Gaussian Geographical Topics 4Christoph Carl Kling Topics in topic modelling: Latent variables that explain the co-occurrence of words in documents.
- 5. Detecting Non-Gaussian Geographical Topics 5Christoph Carl Kling Topics in topic modelling: Latent variables that explain the co-occurrence of words in documents. Geographical topics: Latent variables that explain the co-occurrence of words both in documents and in the geographical space.
- 6. Detecting Non-Gaussian Geographical Topics 6Christoph Carl Kling fish, rice seafood, fish seafood, shrimp lobster, wine seafood, fish, salmon fish, salmon, wine seafood, shrimp lobster, seafood, shrimp coffee coffee, wine coffee wine wine pizza, wine pizza, wine pasta, wine pasta, shrimp lobster, shrimp seafood, shrimp Tagged photographies with geo-coordinates
- 7. Detecting Non-Gaussian Geographical Topics 7Christoph Carl Kling fish, rice seafood, fish seafood, shrimp lobster, wine seafood, fish, salmon fish, salmon, wine seafood, shrimp lobster, seafood, shrimp coffee coffee, wine coffee italian, wine wine pizza, wine italian, pizza, wine pasta, wine pasta, shrimp seafood fish lobster shrimp crab wine salmon wine pizza coffee italian pasta seafood, shrimp lobster, shrimp
- 8. Detecting Non-Gaussian Geographical Topics 8Christoph Carl Kling Existing Approaches
- 9. Detecting Non-Gaussian Geographical Topics 9Christoph Carl Kling fish, rice lobster, shrimp seafood, fish seafood, shrimp lobster, wine seafood, fish, salmon seafood, shrimp fish, salmon, wine seafood, shrimp lobster, seafood, shrimp coffee coffee, wine coffee italian, wine wine pizza, wine italian, pizza, wine pasta, wine pasta, shrimp shrimp fish rice seafood lobster wine pizza coffee italian pasta fish seafood salmon shrimp wine seafood shrimp lobster lobster seafood fish salmon wineGeoFolk, S. Sizov 2010
- 10. Detecting Non-Gaussian Geographical Topics 10Christoph Carl Kling fish, rice lobster, shrimp seafood, fish seafood, shrimp lobster, wine seafood, fish, salmon seafood, shrimp fish, salmon, wine seafood, shrimp lobster, seafood, shrimp coffee coffee, wine coffee italian, wine wine pizza, wine italian, pizza, wine pasta, wine pasta, shrimp seafood fish lobster shrimp crab wine salmon wine pizza coffee italian pasta LGTA, Z. Yin et al., 2011
- 11. Detecting Non-Gaussian Geographical Topics 11Christoph Carl Kling fish, rice lobster, shrimp seafood, fish seafood, shrimp lobster, wine seafood, fish, salmon seafood, shrimp fish, salmon, wine seafood, shrimp lobster, seafood, shrimp coffee coffee, wine coffee italian, wine wine pizza, wine italian, pizza, wine pasta, wine pasta, shrimp seafood fish lobster shrimp crab wine salmon wine pizza coffee italian pasta A. Ahmed, L. Hong and A. Smola, 2013
- 12. Detecting Non-Gaussian Geographical Topics 12Christoph Carl Kling Our Approach
- 13. Detecting Non-Gaussian Geographical Topics 13Christoph Carl Kling Cultural areas, country borders, geographical features and other geographical observations exhibit complex spatial distributions wikipedia.org
- 14. Detecting Non-Gaussian Geographical Topics 17Christoph Carl Kling fish, rice lobster, shrimp seafood, fish seafood, shrimp lobster, wine seafood, fish, salmon seafood, shrimp fish, salmon, wine seafood, shrimp lobster, seafood, shrimp coffee coffee, wine coffee italian, wine wine pizza, wine italian, pizza, wine pasta, wine pasta, shrimp Clustering: E.g. mixture of Gaussian/Fisher distributions
- 15. Detecting Non-Gaussian Geographical Topics 18Christoph Carl Kling fish, rice lobster, shrimp seafood, fish seafood, shrimp lobster, wine seafood, fish, salmon seafood, shrimp fish, salmon, wine seafood, shrimp lobster, seafood, shrimp coffee coffee, wine coffee italian, wine wine pizza, wine italian, pizza, wine pasta, wine pasta, shrimp seafood fish lobster shrimp crab wine salmon wine pizza coffee italian pasta
- 16. Detecting Non-Gaussian Geographical Topics 19Christoph Carl Kling
- 17. Detecting Non-Gaussian Geographical Topics 20Christoph Carl Kling Adjacency: Delaunay triangulation K-NN …
- 18. Detecting Non-Gaussian Geographical Topics 21Christoph Carl Kling fish, rice lobster, shrimp seafood, fish seafood, shrimp lobster, wine seafood, fish, salmon seafood, shrimp fish, salmon, wine seafood, shrimp lobster, seafood, shrimp coffee coffee, wine coffee italian, wine wine pizza, wine italian, pizza, wine pasta, wine pasta, shrimp seafood fish lobster shrimp crab wine salmon wine pizza coffee italian pasta
- 19. Detecting Non-Gaussian Geographical Topics 22Christoph Carl Kling Cluster adjacency Dependencies of document- specific topic distributions Exchange of topic information between clusters
- 20. Detecting Non-Gaussian Geographical Topics 23Christoph Carl Kling Exchange of topic information between clusters
- 21. Detecting Non-Gaussian Geographical Topics 24Christoph Carl Kling Exchange of topic information between clusters
- 22. Detecting Non-Gaussian Geographical Topics 25Christoph Carl Kling Exchange of topic information between clusters
- 23. Detecting Non-Gaussian Geographical Topics 26Christoph Carl Kling Exchange of topic information between clusters
- 24. Detecting Non-Gaussian Geographical Topics 27Christoph Carl Kling γ M N L H G G α0 G Al j 0 θjn w η s d l δl L: #regions M: #documents in cluster N: #words in document G :⁰ Global topic distribution G : Cluster-topic distribution G : Document-topic distribution s d MGTM
- 25. Detecting Non-Gaussian Geographical Topics 28Christoph Carl Kling Evaluation
- 26. Detecting Non-Gaussian Geographical Topics 29Christoph Carl Kling Datasets Activities: 1.931 photos Landscape: 5.791 photos Manhattan: 28.922 photos Car: 34.707 photos Food: 151.747 photos LGTA, Z. Yin et al., 2011
- 27. Detecting Non-Gaussian Geographical Topics 30Christoph Carl Kling Compared models: - LGTA: Model with regions - Basic model: 3-level Hierarchical Dirichlet Process - MGTM: Basic model plus dynamically smoothed adjacent regions
- 28. Detecting Non-Gaussian Geographical Topics 31Christoph Carl Kling manhattan (100 regions) landscape (200 regions) activities (300 regions) car (500 regions) food (1000 regions) Word Perplexity
- 29. Detecting Non-Gaussian Geographical Topics 32Christoph Carl Kling User Study Food dataset (1000 regions) 31 participants Task: intrusion detection Measure: precision 4 topics avg / median 6 topics avg / median 8 topics avg / median LGTA 0.67 / 0.64 0.57 / 0.57 0.60 / 0.58 Basic model 0.45 / 0.57 0.63 / 0.61 0.64 / 0.58 MGTM 0.79 / 0.80 0.82 / 0.81 0.78 / 0.75
- 30. Detecting Non-Gaussian Geographical Topics 33Christoph Carl Kling west.uni-koblenz.de Research → systems → MGTM west.uni-koblenz.de liveandgov.eu
- 31. Detecting Non-Gaussian Geographical Topics 34Christoph Carl Kling Thank you! Questions? Contact: c@c-kling.de
- 32. Detecting Non-Gaussian Geographical Topics 35Christoph Carl Kling Summary • Geographical topics often exhibit a complex spatial distribution • The detection of such complex topics can be supported • The dynamic smoothing of adjacent regions leads to an evolutionary creation and spread of topics during inference
- 33. Detecting Non-Gaussian Geographical Topics 36Christoph Carl Kling ReferencesReferences Hierarchical Dirichlet processes by: Y. W. Teh, M. I. Jordan, M. J. Beal, and D. M. Blei In: Journal of the American Statistical Association, Vol. 101 (2006) , p. 1566-1581. GeoFolk: latent spatial semantics in web 2.0 social media. by: Sergej Sizov In: WSDM ACM (2010) , p. 281-290. Geographical topic discovery and comparison. by: Zhijun Yin, Liangliang Cao, Jiawei Han, Chengxiang Zhai, and Thomas S. Huang In: WWW ACM (2011) , p. 247-256. A Nonparametric Bayesian Model of Multi-Level Category Learning. by: Kevin Robert Canini, and Thomas L. Griffiths In: AAAI AAAI Press (2011) .