Advertisement

Multi-source Transfer Learning

  • Zhengming Ding
  • Handong Zhao
  • Yun Fu
Chapter
Part of the Advanced Information and Knowledge Processing book series (AI&KP)

Abstract

Nowadays, it is common to see multiple sources available for knowledge transfer, each of which, however, may not include complete classes information of the target domain. Naively merging multiple sources together would lead to inferior results due to the large divergence among multiple sources. In this chapter, we attempt to utilize incomplete multiple sources for effective knowledge transfer to facilitate the learning task in target domain.

References

  1. Belkin M, Niyogi P, Sindhwani V (2006) Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J Mach Learn Res 7:2399–2434MathSciNetzbMATHGoogle Scholar
  2. Ben-David S, Blitzer J, Crammer K,  Pereira F (2007) Analysis of representations for domain adaptation. In: Advances in neural information processing systems. pp 137–144Google Scholar
  3. Boumal N, Mishra B, Absil P-A, Sepulchre R (2014) Manopt, a Matlab toolbox for optimization on manifolds. J Mach Learn Res 15:1455–1459. http://www.manopt.org
  4. Cai J-F, Candès EJ, Shen Z (2010) A singular value thresholding algorithm for matrix completion. SIAM J Optim 20(4):1956–1982MathSciNetCrossRefGoogle Scholar
  5. Cai D, He X, Han J (2007) Spectral regression: a unified approach for sparse subspace learning. In: IEEE international conference on data mining. IEEE, pp 73–82Google Scholar
  6. Cheng L, Pan SJ (2014) Semi-supervised domain adaptation on manifolds. IEEE Trans Neural Netw Learn Syst 25(12):2240–2249CrossRefGoogle Scholar
  7. Coppersmith D, Winograd S (1987) Matrix multiplication via arithmetic progressions. In: Proceedings of the nineteenth annual ACM symposium on theory of computing. ACM, pp 1–6Google Scholar
  8. Ding Z, Shao M, Fu Y (2014) Latent low-rank transfer subspace learning for missing modality recognition. In: Proceedings of the 28th AAAI conference on artificial intelligenceGoogle Scholar
  9. Ding Z, Shao M, Fu Y (2015) Deep low-rank coding for transfer learning. In: International joint conference on artificial intelligence. pp 3453–3459Google Scholar
  10. Ding Z, Shao M, Fu Y (2015) Missing modality transfer learning via latent low-rank constraint. IEEE Trans Image Process 24(11):4322–4334MathSciNetCrossRefGoogle Scholar
  11. Ding Z, Shao M, Fu Y (2016) Transfer learning for image classification with incomplete multiple sources. In: International joint conference on neural networks. IEEEGoogle Scholar
  12. Ding Z, Shao M, Fu Y (2018) Incomplete multisource transfer learning. IEEE Trans Neural Netw Learn Syst 29(2):310–323MathSciNetCrossRefGoogle Scholar
  13. Duan L, Xu D, Tsang IW (2012) Domain adaptation from multiple sources: a domain-dependent regularization approach. IEEE Trans Neural Netw Learn Syst 23(3):504–518CrossRefGoogle Scholar
  14. Fernando B, Habrard A, Sebban M, Tuytelaars T (2013) Unsupervised visual domain adaptation using subspace alignment. In: IEEE international conference on computer vision. pp 2960–2967Google Scholar
  15. Ge L, Gao J, Zhang A (2013) Oms-tl: a framework of online multiple source transfer learning. In: Proceedings of the 22nd ACM international conference on conference on information & knowledge management. pp 2423–2428Google Scholar
  16. Ge L, Gao J, Ngo H, Li K, Zhang A (2014) On handling negative transfer and imbalanced distributions in multiple source transfer learning. Stat Anal Data Min: ASA Data Sci J 7(4):254–271MathSciNetCrossRefGoogle Scholar
  17. Gong B, Shi Y, Sha F, Grauman K (2012) Geodesic flow kernel for unsupervised domain adaptation. In: IEEE conference on computer vision and pattern recognition. pp 2066–2073Google Scholar
  18. Gopalan R, Li R, Chellappa R (2014) Unsupervised adaptation across domain shifts by generating intermediate data representations. IEEE Trans Pattern Anal Mach Intell 36(11):2288–2302CrossRefGoogle Scholar
  19. He J, Lawrence R (2011) A graph-based framework for multi-task multi-view learning. In: International conference on machine learning. pp 25–32Google Scholar
  20. He X, Niyogi P (2003) Locality preserving projections. In: Neural information processing systems, vol  16. p 153Google Scholar
  21. Hoffman J, Kulis B, Darrell T, Saenko K (2012) Discovering latent domains for multisource domain adaptation. In: European conference on computer vision. Springer, Berlin, pp 702–715CrossRefGoogle Scholar
  22. Jhuo I-H, Liu D, Lee D, Chang S-F (2012) Robust visual domain adaptation with low-rank reconstruction. In: IEEE conference on computer vision and pattern recognition. pp 2168–2175Google Scholar
  23. Jia C, Kong Y, Ding Z, Fu YR (2014) Latent tensor transfer learning for rgb-d action recognition. In: Proceedings of the 22nd ACM international conference on multimedia. ACM, pp 87–96Google Scholar
  24. Li J, Najmi A, Gray RM (2000) Image classification by a two-dimensional hidden markov model. IEEE Trans Signal Process 48(2):517–533CrossRefGoogle Scholar
  25. Lin Z, Chen M, Ma Y (2010) The augmented Lagrange multiplier method for exact recovery of corrupted low-rank matrices. arXiv:1009.5055
  26. Lin Z, Liu R, Su Z (2011) Linearized alternating direction method with adaptive penalty for low-rank representation. In: Neural information processing systems. pp 612–620Google Scholar
  27. Liu G, Yan S (2011) Latent low-rank representation for subspace segmentation and feature extraction. In: IEEE international conference on computer vision. pp 1615–1622Google Scholar
  28. Liu G, Lin Z, Yan S, Sun J, Yu Y, Ma Y (2013) Robust recovery of subspace structures by low-rank representation. IEEE Trans Pattern Anal Mach Intell 35(1):171–184CrossRefGoogle Scholar
  29. Long M, Wang J, Ding G, Pan S, Yu P (2014) Adaptation regularization: a general framework for transfer learning. IEEE Trans Knowl Data Eng 26(5):1076–1089CrossRefGoogle Scholar
  30. Long M, Wang M, Ding G, Sun J, Yu P (2014) Transfer joint matching for unsupervised domain adaptation. In: IEEE conference on computer vision and pattern recognition. pp 1410–1417Google Scholar
  31. Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345–1359CrossRefGoogle Scholar
  32. Saenko K, Kulis B, Fritz M, Darrell T (2010) Adapting visual category models to new domains. In: European conference on computer vision. Springer, pp 213–226Google Scholar
  33. Shao M, Kit D, Fu Y (2014) Generalized transfer subspace learning through low-rank constraint. Int J Comput Vis 1–20Google Scholar
  34. Shao L, Zhu F, Li X (2015) Transfer learning for visual categorization: a survey. IEEE Trans Neural Netw Learn Syst 26(5):1019–1034MathSciNetCrossRefGoogle Scholar
  35. Shekhar S, Patel VM, Nguyen HV, Chellappa R (2013) Generalized domain-adaptive dictionaries. In: IEEE conference on computer vision and pattern recognition. pp 361–368Google Scholar
  36. Sun Q, Chattopadhyay R, Panchanathan S, Ye J (2011) A two-stage weighting framework for multi-source domain adaptation. In: Advances in neural information processing systems. pp 505–513Google Scholar
  37. Yan S, Xu D, Zhang B, Zhang H-J, Yang Q, Lin S (2007) Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans Pattern Anal Mach Intell 29(1):40–51CrossRefGoogle Scholar
  38. Yang J, Yin W, Zhang Y, Wang Y (2009) A fast algorithm for edge-preserving variational multichannel image restoration. SIAM J Imaging Sci 2(2):569–592MathSciNetCrossRefGoogle Scholar
  39. Yang L, Jing L, Yu J, Ng MK (2015) Learning transferred weights from co-occurrence data for heterogeneous transfer learning. IEEE Trans Neural Netw Learn Syst PP(99):1–1Google Scholar
  40. Yao Y, Doretto G (2010) Boosting for transfer learning with multiple sources. In: IEEE conference on computer vision and pattern recognition. pp 1855–1862Google Scholar
  41. Yu C-NJ, Joachims T (2009) Learning structural svms with latent variables. In: The 26th annual international conference on machine learning. pp 1169–1176Google Scholar
  42. Zhang K, Gong M, Schölkopf B (2015) Multi-source domain adaptation: a causal view. In: Twenty-ninth AAAI conference on artificial intelligence. pp 3150–3157Google Scholar
  43. Zhou P, Lin Z, Zhang C (2016) Integrated low-rank-based discriminative feature learning for recognition. IEEE Trans Neural Netw Learn Syst 27(5):1080–1093MathSciNetCrossRefGoogle Scholar
  44. Zhu F, Shao L (2014) Weakly-supervised cross-domain dictionary learning for visual recognition. Int J Comput Vis 109(1–2):42–59CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Indiana University-Purdue University IndianapolisIndianapolisUSA
  2. 2.Adobe ResearchSan JoseUSA
  3. 3.Northeastern UniversityBostonUSA

Personalised recommendations