1. Intelligent Database Systems Lab Presenter : Kung, Chien-Hao Authors : Kerstin Bunte, Barbara Hammer, Thomas Villmann, Michael Biehl, Axel Wismuller 2011, NN Neighbor embedding XOM for dimension reduction and visualization

2. Intelligent Database Systems Lab Outlines Motivation Objectives Methodology Experiments Conclusions Comments

3. Intelligent Database Systems Lab Motivation A novel approach to topology learning: XOM XOM supports both structure-preserving dimensionality reduction and data clustering. There is no restriction whatsoever on the distance measures used in XOM.

4. Intelligent Database Systems Lab Objectives Create a conceptual link between: Fast sequential online learning known from topology-preserving mappings Principled direct divergence optimization approaches. Such as SNE and t-SNE

5. Intelligent Database Systems Lab Methodology-Framework XOM Define as the best match input vector Adaptation rule SNE t-SNE

6. Intelligent Database Systems Lab Methodology Gaussian neighborhood T-distribution

7. Intelligent Database Systems Lab Methodology

8. Intelligent Database Systems Lab Experiments-Parameter setting

9. Intelligent Database Systems Lab Experiments-Complexity

10. Intelligent Database Systems Lab Experiments

11. Intelligent Database Systems Lab Experiments-USPS data set

12. Intelligent Database Systems Lab Experiments -cat cortex and protein data set

13. Intelligent Database Systems Lab Experiments -cat cortex and protein data set

14. Intelligent Database Systems Lab Experiments -cat cortex and protein data set

15. Intelligent Database Systems Lab Experiments

16. Intelligent Database Systems Lab Conclusions NE-XOM as a competitive trade-off between: – High embedding quality – Low computational expense NE-XOM allows the user to incorporate prior knowledge and to adapt the level of detail resolution.

17. Intelligent Database Systems Lab Comments Advantages – This content is expressed clearly. Applications – Dimension reduction.