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

Intelligent Database Systems Lab Outlines Motivation Objectives Methodology Experiments Conclusions Comments

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.

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

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

Intelligent Database Systems Lab Methodology Gaussian neighborhood T-distribution

Intelligent Database Systems Lab Methodology

Intelligent Database Systems Lab Experiments-Parameter setting

Intelligent Database Systems Lab Experiments-Complexity

Intelligent Database Systems Lab Experiments

Intelligent Database Systems Lab Experiments-USPS data set

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

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

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

Intelligent Database Systems Lab Experiments

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.

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