Chang WangChang Wang, Sridhar mahadevanSridhar mahadevan

 Problem Introduction  Problem Definition  Who cares  Previous work & challenges  Contribution  A glance at methods

 Example problem  Input: Three collections of documents in  English (sufficient labels)  Italian (sufficient labels)  Arabic (few labels).  Target: Assign labels to the Arabic documents.  A way: find a common feature space for 3 domains  Shared labels, (sports, military)  No shared documents. (no instance correspondence)  No words translations are available.

English docs Italian Docs Arabic docs Shared label set: {sports, military} No corresponding instances or words Question : Can we construct a common feature space so that we can use English docs and Italian docs to help classify Arabic docs? docword1word2…label 102sports 220military sports docparola1parola2…etichetta 120sports 202military sports doc1 كلمة 2 كلمة … ملصق 102sports 220military 310? 401?

English docs Italian docs Arabic docs docword1word2…label 102sports 220military sports docparola1parola2…etichetta 120sports 202military sports doc1 كلمة 2 كلمة … ملصق 102sports 220military 310? 401? docfeature1feature2…label 102sports 220military sports military sports military 1110? 1201? Common feature space

 Given K input datasets in different domains, with different features, but all of the datasets shared the same label set.  Source domain have sufficient labeled instances.  Target domain have few labeled instances.  Question: Can we construct a common feature space?  So all instances in different domain can be mapped to the same feature space, so that we can perform learning task?

Source k Source 1 Target Common feature space Learning

 Search engine  classify docs, rank docs, find docs topics  Businessman  Customer clustering  Biologist  Match protein

 Target domain have little labels  No instance correspondence  Source domain and target domain have different feature space

 Most work assumes that the source domain and the target domain have the same features.  Manifold regularization  Do not leverage source domain information  Transfer learning based on manifold alignment: use both label and unlabeled instance to learn mapping  require small amount of instance correspondence.

 Transfer learning perspective  Can work on different feature space  Cope with multiple input domain  Can combine with existing domain adaption methods  Manifold alignment perspective  Need no instance correspondence  Use label to learn alignment

English docs Italian docs Arabic docs docword1word2…label 102sports 220military sports docparola1parola2…etichetta 120sports 202military sports doc1 كلمة 2 كلمة … ملصق 102sports 220military 310? 401? docfeature1feature2…label 102sports 220military sports military sports military 1110? 1201? Common feature space