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Presenter : Wei- Hao Huang Authors : Miguel ´ A. Carreira-Perpi˜ n´an ICML , 2010

The Elastic Embedding Algorithm for Dimensionality Reduction. Presenter : Wei- Hao Huang Authors : Miguel ´ A. Carreira-Perpi˜ n´an ICML , 2010. Outlines. Motivation Objectives Methodology Experiments Conclusions Comments. Motivation.

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Presenter : Wei- Hao Huang Authors : Miguel ´ A. Carreira-Perpi˜ n´an ICML , 2010

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  1. The Elastic Embedding Algorithm for Dimensionality Reduction Presenter : Wei-Hao HuangAuthors : Miguel ´ A. Carreira-Perpi˜n´anICML, 2010

  2. Outlines • Motivation • Objectives • Methodology • Experiments • Conclusions • Comments

  3. Motivation • The disadvantage of dimensionality reduction • Difficult to understand their objective function. • Optimisationis costly and prone to local optima.

  4. Objectives • To propose a new dimensionality reduction • More efficient and robust • Further our understanding algorithms

  5. Methodology - Framework Objective function + Laplacianeigenmaps SNE High dimension dataset Elastic Embedding Low dimension data

  6. Methodology – Elastic Embedding • Object function • Gradient of E

  7. Methodology - Study of λ • N=2 • N>2

  8. Methodology – Out of sample • Objective function • Mapping and reconstruction mappings

  9. Experiments – 2D spiral

  10. Experiments – Swiss roll

  11. Experiments – COIL-20 dataset

  12. Conclusions • EE dimensionality reduction improves over SNE methods. • EE produces better quality more quickly and robustly. • All of ideas can be directly applied to SNE, t-SNE and earlier algorithms.

  13. Comments • Advantages • EE improves disadvantage of SNE on different versions • Applications • Dimensionality Reduction

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