1. Semi Supervised Learning Qiang Yang –Adapted from… Thanks –Zhi-Hua Zhou –http://cs.nju.edu.cn/pe ople/zhouzh/ –zhouzh@nju.edu.cnzhouzh@nju.edu.cn –LAMDA Group, –National Laboratory for Novel Software Technology, Nanjing University, China

2. Supervised learning is a typical machine learning setting, where labeled examples are used as training examples decision trees, neural networks, support vector machines, etc. trained model training data label training ? = yes unseen data (Jeff, Professor, 7, ?) label unknown Supervised learning

3. Labeled vs. Unlabeled In many practical applications, unlabeled training examples are readily available but labeled ones are fairly expansive to obtain because labeling the unlabeled examples requires human effort class = “ war” (almost) infinite number of web pages on the Internet ?

4. Three main paradigms for Semi-supervised Learning: Transductive learning: Unlabeled examples are exactly the test examples Active learning: Assume that a user can continue to label data The learner actively selects some unlabeled examples to query from an oracle (assume the learner has some control over the input space) Multi-view Learning Unlabeled examples may be different from the test examples Regularization (minimize error and maximize smoothness) Multi-view Learning and Co-training

5. SSL: Why unlabeled data can be helpful? Suppose the data is well-modeled by a mixture density: Thus, the optimal classification rule for this model is the MAP rule: where and  = {  l } The class labels are viewed as random quantities and are assumed chosen conditioned on the selected mixture component m i  {1,2,…,L} and possibly on the feature value, i.e. according to the probabilities P[c i |x i,m i ] where unlabeled examples can be used to help estimate this term [D.J. Miller & H.S. Uyar, NIPS’96]

6. Transductive SVM Transductive SVM : Taking into account a particular test set and trying to minimize misclassifications of just those particular examples Figure reprinted from [T. Joachims, ICML99] Concretely, using unlabeled examples to help identify the maximum margin hyperplanes

7. Active learning: Getting more from query The labels of the training examples are obtained by querying the oracle. Thus, for the same number of queries, more helpful information can be obtained by actively selecting some unlabeled examples to query Key: To select the unlabeled examples on which the labeling will convey the most helpful information for the learner

8.  Uncertainty sampling Train a single learner and then query the unlabeled instances on which the learner is the least confident [Lewis & Gale, SIGIR’94]  Committee-based sampling Generate a committee of multiple learners and select the unlabeled examples on which the committee members disagree the most [Abe & Mamitsuka, ICML’98; Seung et al., COLT’92] Active Learning: Representative approaches

9. To retrieve images from a (usually large) image database according to user interest very useful in digital library, digital photo album, etc. Active Learning Application: Image retrieval Where are my photos taken at Guilin?

10. Database Text Interface Text Interface Text-based Retrieval Engine  Every image is associated with a text annotation  User poses a keyword  The system retrieves images by matching the keyword with annotations Active Learning: Text-based image retrieval “tiger” query tiger lily white tiger

11. In some applications, there are two sufficient and redundant views, i.e. two attribute sets each of which is sufficient for learning and conditionally independent to the other given the class label e.g. two views for web page classification: 1) the text appearing on the page itself, and 2) the anchor text attached to hyperlinks pointing to this page, from other pages Co-training

12. learner 1 learner 2 X 1 view X 2 view labeled training examples unlabeled training examples labeled unlabeled examples [A. Blum & T. Mitchell, COLT98] Co-training (con’t)

13.  Theoretical analysis [Blum & Mitchell, COLT’98; Dasgupta, NIPS’01; Balcan et al., NIPS’04; etc.]  Experimental studies [Nigam & Ghani, CIKM’00]  New algorithms Co-training without two views [Goldman & Zhou, ICML’00; Zhou & Li, TKDE’05] Semi-supervised regression [Zhou & Li, IJCAI’05]  Applications Statistical parsing [Sarkar, NAACL01; Steedman et al., EACL03; R. Hwa et al., ICML03w] Noun phrase identification [Pierce & Cardie, EMNLP01] Image retrieval [Zhou et al., ECML’04; Zhou et al., TOIS06]

14. Multi-view Learning and Co- training Multi-view learning describes the setting of learning from data where observations are represented by multiple independent sets of features. An example of two views: Features can be split into two sets: –The instance space: –Each instance:

15. Inductive vs.Transductive Transductive: Produce label only for the available unlabeled data. –The output of the method is not a classifier. Inductive: Not only produce label for unlabeled data, but also produce a classifier.

16. An Example of two views Web-page classification: e.g., find homepages of faculty members. –Page text: words occurring on that page: e.g., “research interest”, “teaching” –Hyperlink text: words occurring in hyperlinks that point to that page: e.g., “my advisor”

17. Another Example X1 : job title X2: job description Classifying Jobs for FlipDog

18. Two Views : the set of target function over. : the set of target functions over. : the set of target function over. Instead of learning from, multi-view learning aims to learn a pair of functions from, such that.

19. Co-training Proposed by (Blum and Mitchell 1998) Combine Multi-view learning & semi-supervised learning. Related work: –(Yarowsky 1995) –(Nigam and Ghani, 2000) –(Goldman and Zhou, 2000) –(Abney, 2002) –(Sarkar, 2002) –… Used in document classification, parsing, etc.

20. The Yarowsky Algorithm Iteration: 0 + - A Classifier trained by SL Choose instances labeled with high confidence Iteration: 1 + - Add them to the pool of current labeled training data …… (Yarowsky 1995) Iteration: 2 + -

21. Co-training Assumption 1: compatibility The instance distribution is compatible with the target function if for any with non-zero probability,. Definition: compatibility of with :  Each set of features is sufficient for classification

22. Co-training Assumption 2: conditional independence Definition: A pair of views satisfy view independence when: A classification problem instance satisfies view independence when all pairs satisfy view independence.

23. Co-training Algorithm

24. Co-Training Instances contain two sufficient sets of features –i.e. an instance is x=(x 1,x 2 ) –Each set of features is called a View Two views are independent given the label: Two views are consistent: x x1x1 x2x2 (Blum and Mitchell 1998)

25. Co-Training Iteration: t + - Iteration: t +1 + - …… C1: A Classifier trained on view 1 C2: A Classifier trained on view 2 Allow C1 to label Some instances Allow C2 to label Some instances Add self-labeled instances to the pool of training data

26. Agreement Maximization A side effect of the Co-Training: Agreement between two views. Is it possible to pose agreement as the explicit goal? –Yes. The resulting algorithm: Agreement Boost (Leskes 2005)

27. What if Co-training Assumption Not Perfectly Satisfied? Idea: Want classifiers that produce a maximally consistent labeling of the data If learning is an optimization problem, what function should we optimize? - + + +

28. Other Related Works Multi-view clustering (Bickel & Scheffer 2004) Modified the co-training algorithm by replacing the class variable (class label) with a mixture coefficient to obtain a multi-view clustering algorithm. Manifold co-regularization (Sindhwani et al., 2005) Extended Manifold regularization to multi-view learning. Active multi-view learning (Muslea 2002) Combine active learning and multi-view learning. More related works can be find in the workshop on Multi- view learning in ICML 2005: http://www-ai.cs.uni-dortmund.de/MULTIVIEW2005/index.html

29. Reference A. Blum and T. Mitchell, 1998. “Combining Labeled and Unlabeled Data with Co-Training,” In Proceedings of COLT 1998. D. Yarowsky. Unsupervised word sense disambiguation rivaling supervised methods. In Proceedings of ACL 1995. Nigam, K., & Ghani, R, 2000. Analyzing the effectiveness and applicability of co-training. In Proceedings of CIKM 2000. Steven Abney, 2002. Bootstrapping. In Proceedings of ACL, 2002. Ulf Brefeld and Tobias Scheer. Co-EM support vector learning. In Proceedings ICML, 2004. Steen Bickel and Tobias Scheer. Multi-view clustering. In Proceedings of ICDM, 2004. Sindhwani, V.; Niyogi, P.; and Belkin, M. 2005. A Co-Regularization Approach to Semi-supervised Learning with Multiple Views. In Workshop on Learning with Multiple Views at ICML 2005. Ion Muslea. Active learning with multiple views. PhD thesis, University of Southern California, 2002.