Neural Information Processing Systems - NIPS05 Workshops

Spectral Clustering and Transductive Inference for Graph Data

author: Dengyong Zhou, Microsoft Research

Categories

Top: Computer Science: Machine Learning: Clustering

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*Slides*
0:00	Spectral clustering and transductive inference
0:06	Problem setting of clustering
1:06	An undirected graph
1:19	Graph min-cut: formulism
1:50	Graph min-cut: toy example
2:02	Graph min-cut: toy example1
2:24	Normalized cut: basic intuition
2:55	Normalized cut: formalism
3:20	Normalized cut: formalism1
3:52	Normalized cut: toy example
4:11	Normalized cut: algorithm
4:49	Normalized cut: algorithm1
5:16	How to partition a directed graph?
5:50	How to partition a directed graph?1
6:19	How to partition a directed graph?2
6:55	How to partition a directed graph?3
7:41	Algorithmic challenges in web search engines
8:05	Our solution: intuition
8:31	Our solution: formulism
9:53	Our solution: formulism1
10:59	Our solution: formulism2
11:44	Our solution: formulism3
12:37	Our solution: algorithm
13:20	Our solution: algorithm1
13:53	How to define a random walk
14:50	Transductive inference
15:07	Transductive inference1
15:58	Transductive inference2
16:23	Directionality does contain valuable information!
17:50	Conclusion

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