1. Bridged Refinement for Transfer Learning XING Dikan, DAI Wenyua, XUE Gui-Rong, YU Yong Shanghai Jiao Tong University {xiaobao,dwyak,grxue,yyu}@apex.sjtu.edu.cn

2. Outline Motivation Problem Solution – Assumption – Method – Improvement and Final Solution Experiment Conclusion

3. Overview Motivation Problem Solution – Assumption – Method – Improvement and Final Solution Experiment Conclusion

4. Motivation Email spamming: Whether a given mail is a spam or not. – Training Data – Test Data Pop music basketball football classic music Mailbox:

5. Motivation New events always occur. news in 2006, commercial or politics news in 2007, commercial or politics Solution ? – Labeling new data again and again -- costly Therefore, … We try to utilize those old labeled data but take the shift of distribution into consideration. [Transfer useful information]

6. Overview Motivation Problem Solution – Assumption – Method – Improvement and Final Solution Experiment Some other solutions

7. Problem We want to solve a classification problem. The set of target categories is fixed. Main difference from traditional classification: – The training data and test data are governed by two slightly different distributions. We do not need labeled data in the new test data distribution.

8. Illustrative Example sports music +: normal mail -: spam mail

9. Overview Motivation Problem Solution – Assumption – Method – Improvement and Final Solution Experiment Some other solutions

10. Overview Motivation Problem Solution – Assumption – Method – Improvement and Final Solution Experiment Some other solutions

11. Assumption P(c|d) doesn’t changes: P train (c|d) = P test (c|d) Since – The set of target categories is fixed. – Each target category is definite. P(c|d i ) ~ P(c|d j ), when d i ~ d j. ~ means “similar”, “close to each other” Consistency – Mutual Reinforcement Principle

12. Overview Motivation Problem Solution – Assumption – Method – Improvement and Final Solution Experiment Some other solutions

13. Method: Refinement UConf c : scores of a base classifier, coarse-gained (Unrefined Confidence score of category c) M: adjacent matrix. M ij = 1 if d i is a neighbor of d j (then row L1 normalized). RConf c : Refined Confidence score of category c. Mutual reinforcement principle yields: RConf c = α M RConf c + (1-α) UConf c where α is a trade-off coefficient.

14. Method: Refinement Refinement can be regarded as reaching a consistency under the mixture distribution. Why not try to reach a consistency under the distribution of the test data?

15. Illustrative Example

17. Overview Motivation Problem Solution – Assumption – Method – Improvement and Final Solution Experiment Some other solutions

18. Method: Bridged Refinement Bridged Refinement – Refine towards the mixture distribution – Refine towards the target distribution.

19. Outline Motivation Problem Solution – Assumption – Method – Improvement and Final Solution Experiment Conclusion

20. Experiment Data set Base classifiers Different refinement styles Performance Parameter sensitivity

21. Experiment: Data set Source – SRAA Simulated autos (simauto) Simulated aviation (simaviation) Real autos (realauto) Real aviation (realaviation) – 20 Newsgroup Top level categories: rec, talk, sci, comp – Reuters-21578 Top level categories: org, places, people

22. Experiment: Data set Re-construction – 11 data sets PositiveNegative Training Data Test Data

23. Experiment: Base classifier Supervised – Generative model: Naïve Bayes classifier – Discriminative model: Support vector machines Semi-supervised: – Transductive support vector machines

24. Experiment: Refinement Style No refinement (base) One step – Refine directly on the test distribution (Test) – Refine on the mixture distribution only (Mix) Two steps – Bridged Refinement (Bridged)

25. Performance: On SVM Base Test Mix Bridged Test (2 nd ), Mix(3 rd ) v.s. Base (1 st ) Test (2 nd ) v.s. Bridged (1 st ) ： – Different start point

26. Performance: NB and TSVM

27. Parameter: K Whether di is regarded as a neighbor of dj is decided by checking whether di is in dj’s k-nearest neighbor set.

28. Parameter: α Error rate Vs. Different alpha

29. Convergence The refinement formula can be solved in a close manner or an iterative manner.

30. Outline Motivation Problem Solution – Assumption – Method – Improvement and Final Solution Experiment Conclusion

31. Task: Transfer useful information from training data to the same classification task of the test data, while training and test data are governed by two different distributions. Approach: Take the mixture distribution as a bridge and make a two-step refinement.

32. Thank you Please ask in slow and simple English

33. Backup 1: Tranductive The boundary after either step of refinement are actually never calculated explicitly. It is hidden in the refined labels of each data points. I draw it in the examples explicitly is for a clearer illustration only.

34. Backup 2: n-step One important problem left unsolved by us: – How to describe a distribution \lembda D_train + (1-\lembda) D_test ? – One solution is sampling in a generative manner. But this makes the result depends on each random number picked up in the generative process. It may cause the solution not very stable and hard to repeat.

35. Backup 3: Why mutual reinforcement principle ? If d_j has a high confidence to be in category c, then d_i, the neigbhor of d_j should also receive a high confidence score.