1. Selected Applications of Transfer Learning
杨强，Qiang Yang
Department of Computer Science and Engineering
The Hong Kong University of Science and Technology
Hong Kong 1

2. Case 1: 目标变化 目标迁移 Target Class Changes  Target Transfer Learning
Training: 2 class problem
Testing: 10 class problem.
Traditional methods fail
Solution: find out what is not changed bewteen training and testing

3. Our Work Cross-Domain Learning Translated Learning
TrAdaBoosting (ICML 2007)
Co-Clustering based Classification (SIGKDD 2007)
TPLSA (SIGIR 2008)
NBTC (AAAI 2007)
Translated Learning
Cross-lingual classification (in WWW 2008)
Cross-media classification (In NIPS 2008)
Unsupervised Transfer Learning
Self-taught clustering (ICML 2008)
We have published several papers about cross-domain transfer learning in top conferences in the past year, including ICML, AAAI, KDD, PKDD, SIGIR, WWW. One of our papers won PKDD 2007 Best Student Paper Award last year. 3

4. Our Work (cont)
Wenyuan Dai, Yuqiang Chen, Gui-Rong Xue, Qiang Yang, and Yong Yu. Translated Learning. In Proceedings of Twenty-Second Annual Conference on Neural Information Processing Systems (NIPS 2008), December 8, 2008, Vancouver, British Columbia, Canada. (Link)
Xiao Ling, Wenyuan Dai, Gui-Rong Xue, Qiang Yang, and Yong Yu. Cross-Domain Spectral Learning. In Proceedings of the Fourteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM KDD 2008), Las Vegas, Nevada, USA, August 24-27, (PDF)
Wenyuan Dai, Qiang Yang, Gui-Rong Xue and Yong Yu. Self-taught Clustering. In Proceedings of the 25th International Conference on Machine Learning (ICML 2008), Helsinki, Finland, 5-9 July, (PDF)
Wenyuan Dai, Qiang Yang, Gui-Rong Xue and Yong Yu. Boosting for Transfer Learning. In  Proceedings of The 24th Annual International Conference on Machine Learning   (ICML'07) Corvallis, Oregon, USA, June 20-24, (PDF)
Wenyuan Dai, Gui-Rong Xue, Qiang Yang and Yong Yu. Co-clustering based Classification for Out-of-domain Documents. In Proceedings of the Thirteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM KDD'07), San Jose, California, USA, Aug 12-15, Pages (PDF)
Dou Shen, Jian-Tao Sun, Qiang Yang and Zheng Chen. Building Bridges for Web Query Classification. In Proceedings of the 29th ACM International Conference on Research and Development in Information Retrieval (ACM SIGIR 06). Seattle, USA, August 6-11, Pages (PDF)  

5. Query Classification and Online Advertisement
ACM KDDCUP 05 Winner
SIGIR 06
ACM Transactions on Information Systems Journal 2006
Joint work with Dou Shen, Jiantao Sun and Zheng Chen

6. QC as Machine Learning Inspired by the KDDCUP’05 competition
Classify a query into a ranked list of categories
Queries are collected from real search engines
Target categories are organized in a tree with each node being a category 6

7. Related Works Query Classification/Clustering Document/Query Expansion
Classify the Web queries by geographical locality [Gravano 2003];
Classify queries according to their functional types [Kang 2003];
Beitzel et al. studied the topical classification as we do. However they have manually classified data [Beitzel 2005];
Beeferman and Wen worked on query clustering using clickthrough data respectively [Beeferman 2000; Wen 2001];
Document/Query Expansion
Borrow text from extra data source
Using hyperlink [Glover 2002];
Using implicit links from query log [Shen 2006];
Using existing taxonomies [Gabrilovich 2005];
Query expansion [Manning 2007]
Global methods: independent of the queries
Local methods using relevance feedback or pseudo-relevance feedback 7

8. Target-transfer Learning in QC
Classifier, once trained, stays constant
Target Classes Before
Sports, Politics (European, US, China)
Target Classes Now
Sports (Olympics, Football, NBA), Stock Market (Asian, Dow, Nasdaq), History (Chinese, World) How to allow target to change?
Application:
advertisements come and go,
but our querytarget mapping needs not be retrained!
We call this the target-transfer learning problem

9. Solutions: Query Enrichment + Staged Classification
Solution: Bridging classifier 9

10. Step 1: Query enrichment
Textual information
Category information
Full text
Title
Snippet
Category 10

11. Step 2: Bridging Classifier
Wish to avoid:
When target is changed, training needs to repeat!
Solution:
Connect the target taxonomy and queries by taking an intermediate taxonomy as a bridge 11 11

12. Bridging Classifier (Cont.)
How to connect?
The relation between and
The relation between and
Prior prob. of
The relation between and 12 12

13. Category Selection for Intermediate Taxonomy
Category Selection for Reducing Complexity
Total Probability (TP)
Mutual Information 13 13

14. Experiment ─ Data Sets & Evaluation
ACM KDDCUP
Starting 1997, ACM KDDCup is the leading Data Mining and Knowledge Discovery competition in the world, organized by ACM SIG-KDD.
ACM KDDCUP 2005
Task: Categorize 800K search queries into 67 categories
Three Awards
(1) Performance Award ; (2) Precision Award; (3) Creativity Award
Participation
142 registered groups;
37 solutions submitted from 32 teams
Evaluation data
800 queries randomly selected from the 800K query set
3 human labelers labeled the entire evaluation query set
Evaluation measurements: Precision and Performance (F1)
We won all three. a
14 / 68 14

15. Result of Bridging Classifiers
Performance of the Bridging Classifier with Different Granularity of Intermediate Taxonomy
Using bridging classifier allows the target classes to change freely
no the need to retrain the classifier! 15

16. Summary: Target-Transfer Learning
Intermediate
Class
classify to
Query
Similarity
Target class

17. Cross-Domain Learning
Input
Output
Here is a example about concept-domain transfer.
For example, in the task of classifying air-plain and car, the training data are all about cartoon, while the test data are all real objects.
We know cartoon objects and real objects share the key features about the target objects, while they also have a large amount of distinguished features. The difficulty of this problem is how to find the key features which can describe both cartoon and real objects. 17

18. Case 1 Source Target Target and source domains Many labeled instances
Few labeled instances
Target and source domains
Same feature representation
Same classes Y (binary classes)
Different P(X,Y) distribution

19. TrAdaBoost = Transfer AdaBoost (cont.)
Given
Insufficient labeled data from the target domain (primary data)
Labeled data following a different distribution (auxiliary data)
The auxiliary data are weaker evidence for building the classifier
Target training
source + target
Uniform weights (X)
In ICML 2007, we have one paper which solve the problem of using a large amount of auxiliary data to help the classification in a different domain.
We use the boosting theory to model our algorithm, so our algorithm is called transfer adaboost (or tradaboost).
The basic idea is if we only have very few target training data, we don’t know where the decision boundary is exactly.
If we have some auxiliary data, these data may help us to find the decision boundary.
But the auxiliary data are in low quality, and may tell us something wrong.
For example, in the third figure, the decision boundary based on auxiliary data misclassifies the target training data. So, we have to correct the mistake. 19

20. TrAdaBoost = Transfer AdaBoost (cont.)
Misclassified examples:
increase the weights of the misclassified target data
decrease the weights of the misclassified source data
Our idea is to adjust the weights of misclassified examples.
Specifically,
increase the weights of the misclassified primary data
decrease the weights of the misclassified auxiliary data
as in this figure
So that, the decision boundary moves towards the right direction.
We model our algorithm under the boosting theory. So, we can prove the error bound of our algorithm as show in this theorem. 20

21. TrAdaBoost = Transfer AdaBoost (cont.)
Performance

22. Transfer Learning in Sensor Network Tracking
Received-Signal-Strength (RSS) based localization in an Indoor WiFi environment.
Access point 2
Mobile device
Access point 1
Access point 3
-30dBm
-70dBm
-40dBm
(location_x, location_y)
Where is the mobile device? 22

23. Distribution Changes
The mapping function f learned in the offline phase can be out of date.
Recollecting the WiFi data is very expensive.
How to adapt the model ?
Time
Night time period
Day time period 23

24. Transfer Learning in Wireless Sensor Networks
Transfer across time
Transfer across space
Transfer across device

25. Latent Space based Transfer Learning (Spatial Transfer) Transfer Localization Models across Space [Pan, Yang et al. AAAI 08]
Some labeled data collected in Area A and unlabeled data in B;
Only a few labeled data collected in Area B;
Want to:
Construct a localization model of the whole area (Area A and Area B)

26. Transfer across time Area: 30 X 40 (81 grids) Six time periods:
LeMan:
Static mapping function learnt from offline data;
LeMan2:
Relearn the mapping function from a few online data
LeMan3:
Combine offline and online data as a whole training data to learn the mapping function.
Area: 30 X 40 (81 grids)
Six time periods:
12:30am--01:30am
08:30am--09:30am
12:30pm--01:30pm
04:30pm--05:30pm
08:30pm--09:30pm
10:30pm--11:30pm 26

27. Transfer knowledge via latent manifold learning
Labeled WiFi Data
Labeled WiFi Data
Latent Manifold
Knowledge Propagation

28. VIP Recommendation in Tencent Weibo
Properties:
Friendship relations in Tencent QQ, which is the largest instant messenge network
1. Data Sparsity: limited neighbors for most users
Knowledge
Transfer
2. Heterogeneous Links: symmetric friendship vs. asymmetric following
3. Large Data: 1 billion users and tens of billion links 28

29. Social Relation based Transfer (SORT)
VIP Recommendation Based on One's
1. X: Friendship on QQ
2. S1: User Following Relations on Tencent Weibo
3. S2: VIP Following Relations on Tencent Weibo

30. Social App Recommendation in Tecent Qzone
Other Applications
Social App Recommendation in Tecent Qzone
Qzone ( is the largest social network in China.
Video Recommendation in Tencent Video
Four types of auxiliary data
1. binary ratings
2. social networks
3. context
4. video content
Rating Prediction 30

31. Activity Recognition With sensor data collected on mobile devices
Location
GPS, Wifi, RFID
Context: location, weather, etc.
From GPS, RFID, Bluetooth, etc.
Various models can be used
Non-sequential models:
Naïve Bayes, SVM …
Sequential models:
HMM, CRF …

32. Activity Recognition: Input & Output (Vincent Zheng, A* Sg)
Context and locations
Time, history, current/previous locations, duration, speed,
Object Usage Information
Trained AR Model
Training data from calibration
Calibration Tool: VTrack
Output:
Predicted Activity Labels
Running?
Walking?
Tooth brushing?
Having lunch? 32

33. Datasets: MIT PlaceLab http://architecture. mit. edu/house_n/placelab
MIT PlaceLab Dataset (PLIA2) [Intille et al. Pervasive 2005]
Activities: Common household activities 33

34. Cross Domain Activity Recognition [Zheng, Hu, Yang, Ubicomp 2009]
Challenges:
A new domain of activities without labeled data
Cross-domain activity recognition
Transfer some available labeled data from source activities to help training the recognizer for the target activities.
Cleaning Indoor
Laundry
Dishwashing 34 34

35. How to use the similarities?
Example: sim(“Make Coffee”, “Make Tea”) = 0.6
<Sensor Reading, Activity Name>
Example: <SS, “Make Coffee”>
Example: Pseudo Training Data: <SS, “Make Tea”, 0.6>
Similarity Measure
THE WEB
Target Domain Pseudo Labeled Data
Source Domain Labeled Data
Weighted SVM Classifier 35 35

36. Calculating Activity Similarities
How similar are two activities?
Use Web search results
TFIDF: Traditional IR similarity metrics (cosine similarity)
Example
Mined similarity between the activity “sweeping” and “vacuuming”, “making the bed”, “gardening” 36