Adaptive local dissimilarity measures for discriminative dimension of labeled data

1. Adaptive local dissimilarity measures for discriminative dimension of labeled data Presenter : Kung, Chien-HaoAuthors : Kerstin Bunte, Barbara Hammer, Axel Wismuller,Michael Biehl2010,NC

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

3. Motivation • Dimension reduction embedding in lower dimensions necessarily includes a loss of information. • To explicitly control the information kept by a specific dimension reduction technique are highly desirable.

4. Objectives • The aim of this paper is to combine an adaptive metric and recent visualization techniques towards a discriminative approach.

5. Methodology Charting Stochastic neighbor embedding (SNE) LiRaM LVQ Locally linear embedding (LLE) Exploration observation machine(XOM) Isomap Maximum variance unfolding(MVU)

6. Methodology LiRaM LVQ • Prototype based classifier, extension of LVQ • Modified Euclidean distance: • Adapt local matrices during training(minimize a cost function by gradient descent)

7. Methodology Combination of local linear patches by charting • The charting technique can decompose the sample data into locally linear patches and combine them into a single low-dimensional coordinate system.

8. Methodology Locally linear embedding (LLE) • Locally linear embedding (LLE) uses the criterion of topology preservation for dimension reduction.

9. Methodology Isomap • Isomap is an extension of metric Multi-Dimensional Scaling(MDS) which uses distance preservation as criterion

10. Methodology Stochastic neighbor embedding (SNE) • Stochastic neighbor embedding (SNE) constitutes an unsupervised projection which follows a probability based approach.

11. Methodology Exploration observation machine(XOM) • The exploratory observation machine (XOM) has recently been introduced as a novel computational framework for structure-preserving dimension reduction.

12. Methodology Maximum variance unfolding(MVU) • Maximum variance unfolding(MVU) is a dimension reduction technique which aims at preservation if local distances.

13. Experiments

14. Experiments Three tip star data set

15. Experiments Wine data set

16. Experiments Segmentationdata set

17. Experiments USPSdata set

18. Experiments

19. Conclusions • The results are quite diverse and no single method which is optimum for every case an be identified. • In general, discriminative visualization as introduced in this paper improves all the corresponding unsupervised methods.

20. Comments • Advantages • This paper is easy to read. • Applications • Dimension reduction