Machine Learning, Support Vector Machines, and Large Scale Optimization Workshop

Convex transduction with the normalized cut

author: Tijl De Bie, KU Leuven

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*Slides*
0:02	Convex transduction with the Normalized Cut
1:01	Motivation
1:32	Motivation
2:29	Motivation
3:23	Overview
4:06	The Normalized Cut for Clustering
5:22	The Normalized Cut for Clustering
5:39	The Normalized Cut for Clustering
6:13	The Normalized Cut for Clustering
6:30	The Normalized Cut for Clustering
6:59	The Normalized Cut for Clustering
7:25	The Normalized Cut for Clustering
7:55	Overview
8:02	A spectral relaxation
8:24	A spectral relaxation
9:31	A spectral relaxation
9:50	Overview
9:54	An SDP relaxation
10:48	An SDP relaxation
11:36	An SDP relaxation
11:59	An SDP relaxation
12:33	Overview
12:44	Transduction based on the Normalized Cut
12:56	Transduction based on the Normalized Cut
13:31	Transduction based on the Normalized Cut
13:51	Transduction based on the Normalized Cut
14:30	Transduction based on the Normalized Cut
14:55	Overview
15:16	A combined approach
15:38	A combined approach
16:25	A combined approach
16:45	A combined approach
17:37	A combined approach
17:48	A combined approach
18:26	Overview
18:29	Experiments & conclusions
19:45	Experiments & conclusions
20:23	Experiments & conclusions
20:40	Experiments & conclusions
21:50	Experiments & conclusions
22:08	Experiments & conclusions
23:00	Experiments & conclusions

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