Bridged Refinement for Transfer Learning XING Dikan, DAI Wenyua, XUE Gui-Rong, YU Yong Shanghai Jiao Tong University

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

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

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

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]

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

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.

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

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

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

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

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

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.

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?

Illustrative Example

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

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

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

Experiment Data set Base classifiers Different refinement styles Performance Parameter sensitivity

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 Top level categories: org, places, people

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

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

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)

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

Performance: NB and TSVM

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.

Parameter: α Error rate Vs. Different alpha

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

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

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.

Thank you Please ask in slow and simple English

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.

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.

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.