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.
This chapter is reprinted with permission from IEEE. “Incomplete Multisource Transfer Learning”. IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 2, pp. 310–323, 2018.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Belkin M, Niyogi P, Sindhwani V (2006) Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J Mach Learn Res 7:2399–2434
Ben-David S, Blitzer J, Crammer K, Pereira F (2007) Analysis of representations for domain adaptation. In: Advances in neural information processing systems. pp 137–144
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
Cai J-F, Candès EJ, Shen Z (2010) A singular value thresholding algorithm for matrix completion. SIAM J Optim 20(4):1956–1982
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 73–82
Cheng L, Pan SJ (2014) Semi-supervised domain adaptation on manifolds. IEEE Trans Neural Netw Learn Syst 25(12):2240–2249
Coppersmith D, Winograd S (1987) Matrix multiplication via arithmetic progressions. In: Proceedings of the nineteenth annual ACM symposium on theory of computing. ACM, pp 1–6
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 intelligence
Ding Z, Shao M, Fu Y (2015) Deep low-rank coding for transfer learning. In: International joint conference on artificial intelligence. pp 3453–3459
Ding Z, Shao M, Fu Y (2015) Missing modality transfer learning via latent low-rank constraint. IEEE Trans Image Process 24(11):4322–4334
Ding Z, Shao M, Fu Y (2016) Transfer learning for image classification with incomplete multiple sources. In: International joint conference on neural networks. IEEE
Ding Z, Shao M, Fu Y (2018) Incomplete multisource transfer learning. IEEE Trans Neural Netw Learn Syst 29(2):310–323
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):504–518
Fernando B, Habrard A, Sebban M, Tuytelaars T (2013) Unsupervised visual domain adaptation using subspace alignment. In: IEEE international conference on computer vision. pp 2960–2967
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 2423–2428
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):254–271
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 2066–2073
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):2288–2302
He J, Lawrence R (2011) A graph-based framework for multi-task multi-view learning. In: International conference on machine learning. pp 25–32
He X, Niyogi P (2003) Locality preserving projections. In: Neural information processing systems, vol 16. p 153
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 702–715
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 2168–2175
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 87–96
Li J, Najmi A, Gray RM (2000) Image classification by a two-dimensional hidden markov model. IEEE Trans Signal Process 48(2):517–533
Lin Z, Chen M, Ma Y (2010) The augmented Lagrange multiplier method for exact recovery of corrupted low-rank matrices. arXiv:1009.5055
Lin Z, Liu R, Su Z (2011) Linearized alternating direction method with adaptive penalty for low-rank representation. In: Neural information processing systems. pp 612–620
Liu G, Yan S (2011) Latent low-rank representation for subspace segmentation and feature extraction. In: IEEE international conference on computer vision. pp 1615–1622
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):171–184
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):1076–1089
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 1410–1417
Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345–1359
Saenko K, Kulis B, Fritz M, Darrell T (2010) Adapting visual category models to new domains. In: European conference on computer vision. Springer, pp 213–226
Shao M, Kit D, Fu Y (2014) Generalized transfer subspace learning through low-rank constraint. Int J Comput Vis 1–20
Shao L, Zhu F, Li X (2015) Transfer learning for visual categorization: a survey. IEEE Trans Neural Netw Learn Syst 26(5):1019–1034
Shekhar S, Patel VM, Nguyen HV, Chellappa R (2013) Generalized domain-adaptive dictionaries. In: IEEE conference on computer vision and pattern recognition. pp 361–368
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 505–513
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):40–51
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):569–592
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):1–1
Yao Y, Doretto G (2010) Boosting for transfer learning with multiple sources. In: IEEE conference on computer vision and pattern recognition. pp 1855–1862
Yu C-NJ, Joachims T (2009) Learning structural svms with latent variables. In: The 26th annual international conference on machine learning. pp 1169–1176
Zhang K, Gong M, Schölkopf B (2015) Multi-source domain adaptation: a causal view. In: Twenty-ninth AAAI conference on artificial intelligence. pp 3150–3157
Zhou P, Lin Z, Zhang C (2016) Integrated low-rank-based discriminative feature learning for recognition. IEEE Trans Neural Netw Learn Syst 27(5):1080–1093
Zhu F, Shao L (2014) Weakly-supervised cross-domain dictionary learning for visual recognition. Int J Comput Vis 109(1–2):42–59
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Ding, Z., Zhao, H., Fu, Y. (2019). Multi-source Transfer Learning. In: Learning Representation for Multi-View Data Analysis. Advanced Information and Knowledge Processing. Springer, Cham. https://doi.org/10.1007/978-3-030-00734-8_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-00734-8_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00733-1
Online ISBN: 978-3-030-00734-8
eBook Packages: Computer ScienceComputer Science (R0)