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Enhancing Dimensionality Reduction with Elastic Embedding Algorithm

Explore the Elastic Embedding Algorithm for efficient and robust dimensionality reduction. This study compares the method with others, demonstrating improved performance and applicability in streamlining optimization processes and avoiding local optima. The presentation covers motivation, objectives, methodology (including Elastic Embedding), experiments with various datasets, and conclusions highlighting the algorithm's advantages over traditional methods like SNE. Discover its potential applications in dimensionality reduction tasks.

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Enhancing Dimensionality Reduction with Elastic Embedding Algorithm

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Presentation Transcript

  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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