Semi-supervised Learning
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Semi-supervised Learning

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  • 1. Semi Supervised Learning
    • Qiang Yang
      • Adapted from…
    • Thanks
      • Zhi-Hua Zhou
      • http://cs.nju.edu.cn/people/zhouzh/
      • [email_address]
      • 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 ? = yes Supervised learning decision trees, neural networks, support vector machines, etc. trained model training data label training unseen data (Jeff, Professor, 7, ?) label unknown
  • 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: [D.J. Miller & H.S. Uyar, NIPS’96] 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
  • 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.
    • 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 query Database Text Interface Text-based Retrieval Engine “ tiger” 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. [A. Blum & T. Mitchell, COLT98] Co-training (con’t) learner 1 learner 2 X 1 view X 2 view labeled training examples unlabeled training examples labeled unlabeled examples labeled unlabeled examples
  • 13. Co-training (con’t)
    • 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 Classifying Jobs for FlipDog X1 : job title X2: job description
  • 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 Choose instances labeled with high confidence Add them to the pool of current labeled training data …… (Yarowsky 1995) Iteration: 0 + - A Classifier trained by SL Iteration: 1 + - 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 x 1 x 2 (Blum and Mitchell 1998)
  • 25. Co-Training Allow C1 to label Some instances Allow C2 to label Some instances Iteration: t + - Iteration: t +1 + - …… C1 : A Classifier trained on view 1 C2 : A Classifier trained on view 2 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.