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Neural Computing and Applications

, Volume 25, Issue 1, pp 15–23 | Cite as

Knowledge transfer across different domain data with multiple views

  • Qi Tan
  • Huifang Deng
  • Pei Yang
Original Article

Abstract

In many real-world applications in the areas of data mining, the distributions of testing data are different from that of training data. And on the other hand, many data are often represented by multiple views which are of importance to learning. However, little work has been done for it. In this paper, we explored to leverage the multi-view information across different domains for knowledge transfer. We proposed a novel transfer learning model which integrates the domain distance and view consistency into a 2-view support vector machine framework, namely DV2S. The objective of DV2S is to find the optimal feature mapping such that under the projections the classification margin is maximized, while both the domain distance and the disagreement between multiple views are minimized simultaneously. Experiments showed that DV2S outperforms a variety of state-of-the-art algorithms.

Keywords

Transfer learning Multi-view learning Domain distance View consistency Support vector machine 

References

  1. 1.
    Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345–1359CrossRefGoogle Scholar
  2. 2.
    Dai WY, Xue GR, Yang Q, Yu Y (2007) Co-clustering based classification for out-of-domain documents. In: Berkhin P, Caruana R, Wu X (eds) Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining, 12–15 August 2007, ACM 2007, San Jose, pp 210–219Google Scholar
  3. 3.
    Sarinnapakorn K, Kubat M (2007) Combining sub-classifiers in text categorization: a DST-based solution and a case study. IEEE Trans Knowl Data Eng 19(12):1638–1651CrossRefGoogle Scholar
  4. 4.
    Blitzer J, Dredze M, Pereira F (2007) Biographies, bollywood, boom-boxes and blenders: domain adaptation for sentiment classification. In: Carroll JA, van den Bosch A, Zaenen A (eds) Proceedings of the 45th annual meeting of the association for computational linguistics, 23–30 June 2007. The Association for Computational Linguistics (ACL 2007), Prague, pp 440–447Google Scholar
  5. 5.
    Blitzer J, Kakade S, Foster DP (2011) Domain adaptation with coupled subspaces. In: Gordon G, Dunson D, Dudík M (eds) Proceedings of the fourteenth international conference on artificial intelligence and statistics (AISTATS 2011), 11–13 April 2011. Microtome Publishing, Fort Lauderdale, pp 173–181Google Scholar
  6. 6.
    Pan WK, Xiang EW, Liu NN, Yang Q (2010) Transfer learning in collaborative filtering for sparsity reduction. In: Fox M, Poole D (eds) Proceedings of the twenty-fourth AAAI conference on artificial intelligence (AAAI 2010), 11–15 July 2010. AAAI Press 2010, Atlanta, pp 230–235Google Scholar
  7. 7.
    Ma H, Zhou DY, Liu C, Lyu MR, King I (2011) Recommender systems with social regularization. In: King I, Nejdl W, Li H (eds) Proceedings of the fourth international conference on web search and web data mining (WSDM 2011), 9–12 February 2011. ACM 2011, Hong Kong, pp 287–296Google Scholar
  8. 8.
    Gao W, Cai P, Wong K-F, Zhou AY (2010) Learning to rank only using training data from related domain. In: Crestani F, Marchand-Maillet S, Chen H-H, Efthimiadis EN, Savoy J (eds) Proceedings of the 33rd international ACM SIGIR conference on research and development in information retrieval (SIGIR 2010), 19–23 July 2010. ACM 2010, Geneva, pp 162–169Google Scholar
  9. 9.
    Blum A, Mitchell T (1998) Combining labeled and unlabeled data with co-training. In: Bartlett P, Mansour Y (eds) The eleventh annual conference on computational learning theory (COLT’98), ACM 1998, University of Wisconsin, Madison, pp 92–100Google Scholar
  10. 10.
    Rüping S, Scheffer T (2005) Learning with multiple views. In: De Raedt L, Wrobel S (eds) Machine learning, Proceedings of the twenty-second international conference (ICML 2005), ACM International Conference Proceeding Series 119, 7–11 August 2005. ACM 2005, BonnGoogle Scholar
  11. 11.
    Abney S (2002) Bootstrapping. In: Charniak E, Lin D (eds) Proceedings of the 40th annual meeting of the association for computational linguistics, 6–12 July 2002. The Association for Computational Linguistics 2002, Philadelphia, pp 360–367Google Scholar
  12. 12.
    Zhang D, He JR, Liu Y, Si L, Lawrence RD (2011) Multi-view transfer learning with a large margin approach. In: Apté C, Ghosh J, Smyth P (eds) Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining, 21–24 August 2011. ACM 2011, San Diego, pp 1208–1216Google Scholar
  13. 13.
    Quanz B, Huan J (2009) Large margin transductive transfer learning. In: Wai-Lok Cheung D, Song I-Y, Chu WW, Hu X, Lin JJ (eds) Proceedings of the 18th ACM conference on information and knowledge management (CIKM 2009), 2–6 November 2009. ACM 2009, Hong Kong, pp 1327–1336Google Scholar
  14. 14.
    Joachims T (1999) Transductive inference for text classification using support vector machines. In: Bratko I, Dzeroski S (eds) Proceedings of the sixteenth international conference on machine learning (ICML 1999), 27–30 June 1999. Morgan Kaufmann 1999, Bled, pp 200–209Google Scholar
  15. 15.
    Jiang J, Zhai CX (2007) Instance weighting for domain adaptation in NLP. In: Carroll JA, van den Bosch A, Zaenen A (eds) Proceedings of the 45th annual meeting of the association for computational linguistics, 23–30 June 2007. The Association for Computational Linguistics 2007 (ACL 2007), Prague, pp 264–271Google Scholar
  16. 16.
    Dai WY, Yang Q, Xue GR, Yu Y (2007) Boosting for transfer learning. In: Ghahramani Z (ed) Machine Learning, Proceedings of the twenty-fourth international conference (ICML 2007), 20–24 June 2007. ACM International Conference Proceeding Series 227, ACM 2007, Corvallis, pp 193–200Google Scholar
  17. 17.
    Dayanik AA, Lewis DD, Madigan D, Menkov V, Genkin A (2006) Constructing informative prior distributions from domain knowledge in text classification. In: Efthimiadis EN, Dumais ST, Hawking D, Järvelin K (eds) SIGIR 2006: Proceedings of the 29th annual international ACM SIGIR conference on research and development in information retrieval, 6–11 August 2006. ACM 2006, Seattle, pp 493–500Google Scholar
  18. 18.
    Gretton A, Borgwardt KM, Rasch MJ, Schölkopf B, Smola AJ (2006) A kernel method for the two-sample problem. In: Schölkopf B, Platt JC, Hoffman T (eds) Advances in neural information processing systems 19, Proceedings of the twentieth annual conference on neural information processing systems, 4–7 December 2006. MIT Press 2007, Vancouver, British Columbia, pp 513–520Google Scholar
  19. 19.
    Huang JY, Smola AJ, Gretton A, Borgwardt KM, Schölkopf B (2006) Correcting sample selection bias by unlabeled data. In: Schölkopf B, Platt JC, Hoffman T (eds) Advances in neural information processing systems 19, Proceedings of the twentieth annual conference on neural information processing systems, 4–7 December 2006. MIT Press 2007, Vancouver, British Columbia, pp 601–608Google Scholar
  20. 20.
    Pan SJ, Kwok JT, Yang Q (2008) Transfer learning via dimensionality reduction. In: Fox D, Gomes CP (eds) Proceedings of the twenty-third AAAI conference on artificial intelligence, AAAI 2008, 13–17 July 2008. AAAI Press 2008, Chicago, pp 677–682Google Scholar
  21. 21.
    Dasgupta S, Littman ML, McAllester D (2001) PAC generalization bounds for co-training. In: Dietterich TG, Becker S, Ghahramani Z (eds) Advances in neural information processing systems 14 [Neural Information Processing Systems: Natural and Synthetic, NIPS 2001, 3–8 December 2001]. MIT Press 2001, Vancouver, British Columbia, pp 375–382Google Scholar
  22. 22.
    Chen MM, Weinberger KQ, Blitzer J (2011) Co-training for domain adaptation. In: Shawe-Taylor J, Zemel RS, Bartlett PL, Pereira FCN, Weinberger KQ (eds) Advances in neural information processing systems 24: 25th annual conference on neural information processing systems 2011, 12–14 December 2011. MIT Press 2011, Granada, pp 1–9Google Scholar
  23. 23.
    McCallum AK, Nigam K, Rennie J, Seymore K (2000) Automating the construction of internet portals with machine learning. Inf Retr 3(2):127–163CrossRefGoogle Scholar
  24. 24.
    Salton G, Buckley C (1988) Term-weighting approaches in automatic text retrieval. Inf Process Manag 24(5):513–523CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.Department of Computer ScienceSouth China University of TechnologyGuangzhouChina
  2. 2.Department of Computer ScienceSouth China Normal UniversityGuangzhouChina

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