1. Presented by: Mingyuan Zhou Duke University, ECE October 24, 2012
Dependent Hierarchical Normalized Random Measures for Dynamic Topic Modeling Changyou Chen, Nan Ding and Wray Buntine ICML 2012
Presented by: Mingyuan Zhou
Duke University, ECE
October 24, 2012

2. Introduction
NRM: normalized random measures with independent increments
Superposition, subsampling and point transition of NRM
Dependent hierarchical NRM
Dynamic topic modeling

3. Normalized Random Measures
Poisson process
Completely random measures (CRM)

4. Normalized Random Measures
Completely random measures (CRM)

5. Normalized Random Measures
Slice sampling NRMs
Ref: Griffin, J.E. and Walker, S.G. Posterior simulation of normalized random measure mixtures. J. Comput. Graph. Stat., 2011.

6. Normalized Random Measures
Normalized generalized gamma process

7. Dynamic topic modeling with dependent hierarchical NRMs
Ideas:
Inherit topics from the previous time frame through three dependency operators:
Superposition
Subsampling
Point transition
Generate new topics

8. Dynamic topic modeling with dependent hierarchical NRMs

9. Dynamic topic modeling with dependent hierarchical NRMs

10. Dynamic topic modeling with dependent hierarchical NRMs

11. Dynamic topic modeling with dependent hierarchical NRMs
Properties of the dependence operators

12. Dynamic topic modeling with dependent hierarchical NRMs
Reformulated model

13. Dynamic topic modeling with dependent hierarchical NRMs
Original and reformulated model

14. Sampling
Sampling under the Chinese restaurant metaphor

15. Sampling
Sampling under the Chinese restaurant metaphor

16. Sampling

17. Sampling

18. Sampling

19. Experiments
Power-law in the NGG

20. Experiments

21. Experiments

22. Experiments

23. Experiments

24. Experiments

25. Conclusions