1. Semisupervised Learning A brief introduction

2. Semisupervised Learning Introduction Types of semisupervised learning Paper for review References

3. Semisupervised Learning: Introduction This is an extension to supervised learning. We have two sets of data: Motivation: labeled data is sometimes hard to obtain. Figure obtained from X. Zhu. Semi-Supervised Learning Tutorial. ICML 2007

4. An example from Mars Data Analysis Digital Elevation Map Geomorphic Map Martian landscape Manually drawn geomorphic map of this landscape Geomorphic map shows landforms chosen and defined by a domain expert.

5. Segmentation Segmentation

6. Segmentation: Results. Displayed on an elevation background. 2631 segments homogeneous in slope, curvature and flood.

7. Classification: Labeling. A representative subset of objects are labeled as one of the following six classes: o Plain o Crater Floor o Convex Crater Walls o Concave Crater Walls o Convex Ridges o Concave Ridges Labeled segments.

8. Semisupervised Learning: Introduction How can we learn from unlabeled data at all? The answer lies in the set of assumptions about the unlabeled data distribution. If assumptions are right, an advantage can be obtained using unlabeled data But a decrease in performance is possible if assumptions are incorrect.

9. Semisupervised Learning: Introduction Figure obtained from X. Zhu. Semi-Supervised Learning Tutorial. ICML 2007

10. Semisupervised Learning: Introduction Figure obtained from X. Zhu. Semi-Supervised Learning Tutorial. ICML 2007

11. Semisupervised Learning Introduction Types of semisupervised learning Paper for review References

12. Semisupervised Learning: Types Types of semi-supervised learning: Self-Training Generative Models Graph-Based Algorithms Multi-View Algorithms SVMs

13. Semisupervised Learning: Types Self-Training Figure obtained from X. Zhu. Semi-Supervised Learning Tutorial. ICML 2007

14. Semisupervised Learning: Types Self-Training Variations Figure obtained from X. Zhu. Semi-Supervised Learning Tutorial. ICML 2007

15. Semisupervised Learning: Types Figure obtained from X. Zhu. Semi-Supervised Learning Tutorial. ICML 2007

16. Semisupervised Learning: Types Figure obtained from X. Zhu. Semi-Supervised Learning Tutorial. ICML 2007

17. Semisupervised Learning: Types Figure obtained from X. Zhu. Semi-Supervised Learning Tutorial. ICML 2007

18. Semisupervised Learning: Types Figure obtained from X. Zhu. Semi-Supervised Learning Tutorial. ICML 2007

19. Semisupervised Learning: Types Figure obtained from X. Zhu. Semi-Supervised Learning Tutorial. ICML 2007

20. Semisupervised Learning: Types Figure obtained from X. Zhu. Semi-Supervised Learning Tutorial. ICML 2007

21. Semisupervised Learning: Types Figure obtained from X. Zhu. Semi-Supervised Learning Tutorial. ICML 2007

22. Semisupervised Learning: Types Graph-Based Models Figure obtained from X. Zhu. Semi-Supervised Learning Tutorial. ICML 2007

23. Semisupervised Learning: Types Graph-Based Models Figure obtained from X. Zhu. Semi-Supervised Learning Tutorial. ICML 2007

24. Semisupervised Learning: Types Multi-View Algorithms Figure obtained from X. Zhu. Semi-Supervised Learning Tutorial. ICML 2007

25. Semisupervised Learning: Types Multi-View Algorithms Figure obtained from X. Zhu. Semi-Supervised Learning Tutorial. ICML 2007

26. Semisupervised Learning: Types SVMs Figure obtained from X. Zhu. Semi-Supervised Learning Tutorial. ICML 2007

27. Semisupervised Learning: Types Figure obtained from X. Zhu. Semi-Supervised Learning Tutorial. ICML 2007

28. Semisupervised Learning: Types A comparison of different approaches: Self-Training: Method is simple, but early mistakes propagate down and can be very harmful. Generative Models: great method if prob. model is correct, but it is difficult to verify model correctness. In fact, unlabeled data may yield a decrease in accuracy if model is wrong. Graph-Based Models: good solid mathematical solution if a graph is a good representative of the data distribution. Multi-view Method: simple method that is less sensitive to errors in classification, but there may not be a natural split of the features. SVMs: it can be used wherever SVM is applicable; but may fall into a local maxima, and optimization is hard.

29. Semisupervised Learning Introduction Types of semisupervised learning Paper for review References

30. Semisupervised Learning: Paper Unlabeled data: Now it helps, now it doesn’t by Singh, et. al. Problem: analyze when semi-supervised learning helps to improve generalization performance. Figure obtained from: Singh, et. al. Unlabeled data: now it helps, now it doesn’t. NIPS (2008).

31. Semisupervised Learning: Paper Unlabeled data: Now it helps, now it doesn’t by Singh, et. al. Some terminology that is necessary for the paper follows: “The cluster assumption” means that the distributions of classes in the feature space is smooth on each set D ∈ D. The sets in D are called decision sets. Figure obtained from: Singh, et. al. Unlabeled data: now it helps, now it doesn’t. NIPS (2008).

32. Semisupervised Learning: Paper Unlabeled data: Now it helps, now it doesn’t by Singh, et. al. Figure obtained from: Singh, et. al. Unlabeled data: now it helps, now it doesn’t. NIPS (2008).

33. Semisupervised Learning: Paper Unlabeled data: Now it helps, now it doesn’t by Singh, et. al. Text obtained from: Singh, et. al. Unlabeled data: now it helps, now it doesn’t. NIPS (2008). Main Result: “…if the sets D are discernible using unlabeled data (the margin is large enough compared to average spacing between unlabeled data points), then there exists a semi-supervised learner that can perform as well as a supervised learner with clairvoyant knowledge of the decision sets, provided m ≫ n…”

34. Semisupervised Learning: Paper Unlabeled data: Now it helps, now it doesn’t by Singh, et. al. Figure obtained from: Singh, et. al. Unlabeled data: now it helps, now it doesn’t. NIPS (2008).

35. Semisupervised Learning Introduction Types of semisupervised learning Paper for review References

36. Semisupervised Learning