Multi-view Transformation Learning

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


In this chapter, we would propose two multi-view transformation learning algorithms to solve the classification problem. First of all, we consider the multi-view data have two kinds of manifold structures, i.e., class structure and view structure, then design a dual low-rank decomposition algorithm. Secondly, we assume the domain divergence involves more than one dominant factors, e.g., different view-points, various resolutions and changing illuminations, and explore an intermediate domain could often be found to build a bridge across them to facilitate the learning problem. After that, we propose a Coupled Marginalized Denoising Auto-encoders framework to address the cross-domain problem.


  1. Bao B-K, Liu G, Hong R, Yan S, Xu C (2013) General subspace learning with corrupted training data via graph embedding. IEEE Trans Image Process 22(11):4380–4393MathSciNetCrossRefGoogle Scholar
  2. Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720CrossRefGoogle Scholar
  3. Cai J-F, Candès EJ, Shen Z (2010) A singular value thresholding algorithm for matrix completion. SIAM J Optim 20(4):1956–1982MathSciNetCrossRefGoogle Scholar
  4. Cai X, Wang C, Xiao B, Chen X, Zhou J (2013) Regularized latent least square regression for cross pose face recognition. In: Proceedings of the twenty-third international joint conference on artificial intelligence, pp 1247–1253Google Scholar
  5. Candès EJ, Li X, Ma Y, Wright J (2011) Robust principal component analysis? J ACM 58(3):11MathSciNetCrossRefGoogle Scholar
  6. Chen M, Xu Z, Sha F, Weinberger KQ (2012) Marginalized denoising autoencoders for domain adaptation. In: ICML, pp 767–774Google Scholar
  7. Coates A, Ng AY, Lee H (2011) An analysis of single-layer networks in unsupervised feature learning. In: AISTATS, pp 215–223Google Scholar
  8. Davis JV, Kulis B, Jain P, Sra S, Dhillon IS (2007) Information-theoretic metric learning. In: ICML. ACM, pp 209–216Google Scholar
  9. Ding Z, Fu Y (2014) Low-rank common subspace for multi-view learning. In: ICDM. IEEE, pp 110–119Google Scholar
  10. Ding Z, Fu Y (2016) Robust multi-view subspace learning through dual low-rank decompositions. In: Thirtieth AAAI conference on artificial intelligence, pp 1181–1187Google Scholar
  11. Ding Z, Fu Y (2018) Robust multiview data analysis through collective low-rank subspace. IEEE Trans Neural Netw Learn Syst 29(5):1986–1997MathSciNetCrossRefGoogle Scholar
  12. Ding Z, Shao M, Fu Y (2014) Latent low-rank transfer subspace learning for missing modality recognition. In: Twenty-eighth AAAI conference on artificial intelligence, pp 1192–1198Google Scholar
  13. 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
  14. Ding Z, Suh S, Han J-J, Choi C, Fu Y (2015) Discriminative low-rank metric learning for face recognition. In: 12th IEEE international conference on automatic face and gesture recognitionGoogle Scholar
  15. Ding C, Tao D (2015) A comprehensive survey on pose-invariant face recognition. arXiv:1502.04383
  16. Dong C, Loy CC, He K, Tang X (2014) Learning a deep convolutional network for image super-resolution. In: ECCV. Springer, pp 184–199Google Scholar
  17. Fang R, Tang KD, Snavely N, Chen T (2010) Towards computational models of kinship verification. In: ICIP. IEEE, pp 1577–1580Google Scholar
  18. Farenzena M, Bazzani L, Perina A, Murino V, Cristani M (2010) Person re-identification by symmetry-driven accumulation of local features. In: CVPR. IEEE, pp 2360–2367Google Scholar
  19. Gray D, Tao H (2008) Viewpoint invariant pedestrian recognition with an ensemble of localized features. In: ECCV. Springer, pp 262–275Google Scholar
  20. Gray D, Brennan S, Tao H (2007) Evaluating appearance models for recognition, reacquisition, and tracking. PETS 3(5) CiteseerGoogle Scholar
  21. He X, Niyogi P (2003) Locality preserving projections. In: Neural information processing systems, vol 16, p 153Google Scholar
  22. Hestenes MR (1969) Multiplier and gradient methods. J Optim Theory Appl 4(5):303–320MathSciNetCrossRefGoogle Scholar
  23. Huang D-A, Wang Y-CF (2013) Coupled dictionary and feature space learning with applications to cross-domain image synthesis and recognition. In: ICCV. IEEE, pp 2496–2503Google Scholar
  24. Jing X-Y, Zhu X, Wu F, You X, Liu Q, Yue D, Hu R, Xu B (2015) Super-resolution person re-identification with semi-coupled low-rank discriminant dictionary learning. In: CVPR, pp 695–704Google Scholar
  25. Kan M, Shan S, Zhang H, Lao S, Chen X (2012) Multi-view discriminant analysis. In: Proceedings of European conference on computer vision. Springer, pp 808–821Google Scholar
  26. Koestinger M, Hirzer M, Wohlhart P, Roth PM, Bischof H (2012) Large scale metric learning from equivalence constraints. In: CVPR. IEEE, pp 2288–2295Google Scholar
  27. Li S, Fu Y (2014) Robust subspace discovery through supervised low-rank constraints. In: Proceedings of SIAM international conference on data mining, pp 163–171CrossRefGoogle Scholar
  28. 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
  29. Lin Z, Chen M, Ma Y (2010) The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices. arXiv:1009.5055
  30. Liu G, Lin Z, Yu Y (2010) Robust subspace segmentation by low-rank representation. In: Proceedings of the twenty-seventh international conference on machine learning, pp 663–670Google Scholar
  31. Liu J, Shah M, Kuipers B, Savarese S (2011) Cross-view action recognition via view knowledge transfer. In CVPR. IEEE, pp 3209–3216Google Scholar
  32. Liu S, Yi D, Lei Z, Li SZ (2012) Heterogeneous face image matching using multi-scale features. In: Fifth IAPR international conference on biometrics. IEEE, pp 79–84Google Scholar
  33. 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 35:171–184CrossRefGoogle Scholar
  34. Lu J, Zhou X, Tan Y-P, Shang Y, Zhou J (2014) Neighborhood repulsed metric learni ng for kinship verification. TPAMI 36(2):331–345CrossRefGoogle Scholar
  35. Schroff F, Kalenichenko D, Philbin J (2015) Facenet: a unified embedding for face recognition and clustering. In: CVPR, pp 815–823Google Scholar
  36. Shao M, Xia S, Fu Y (2011) Genealogical face recognition based on UB KinFace database. In: CVPRW. IEEE, pp 60–65Google Scholar
  37. Shao M, Kit D, Fu Y (2014) Generalized transfer subspace learning through low-rank constraint. Int J Comput Vis 109(1–2):74–93MathSciNetCrossRefGoogle Scholar
  38. Shekhar S, Patel V, Nasrabadi N, Chellappa R (2014) Joint sparse representation for robust multimodal biometrics recognition. IEEE Trans Pattern Anal Mach Intell 36(1):113–126CrossRefGoogle Scholar
  39. Si S, Tao D, Geng B (2010) Bregman divergence-based regularization for transfer subspace learning. TKDE 22(7):929–942Google Scholar
  40. Su Y, Li S, Wang S, Fu Y (2014) Submanifold decomposition. IEEE Trans Circuits Syst Video Technol 24(11):1885–1897CrossRefGoogle Scholar
  41. Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3(1):71–86CrossRefGoogle Scholar
  42. Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol P-A (2010) Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. JMLR 11:3371–3408MathSciNetzbMATHGoogle Scholar
  43. Wang S, Fu Y (2016) Face behind makeup. In: AAAIGoogle Scholar
  44. Wang S, Zhang L, Liang Y, Pan Q (2012) Semi-coupled dictionary learning with applications to image super-resolution and photo-sketch synthesis. In: CVPR. IEEE, pp 2216–2223Google Scholar
  45. Wang S, Ding Z, Fu Y (2016) Coupled marginalized auto-encoders for cross-domain multi-view learning. In: Proceedings of the twenty-fifth international joint conference on artificial intelligence. AAAI Press, pp 2125–2131Google Scholar
  46. Wang S, Ding Z, Fu Y (2018) Cross-generation kinship verification with sparse discriminative metric. In: IEEE transactions on pattern analysis and machine intelligenceGoogle Scholar
  47. Weinberger KQ, Blitzer J, Saul LK (2005) Distance metric learning for large margin nearest neighbor classification. In: NIPS, pp 1473–1480Google Scholar
  48. Wright J, Ganesh A, Rao S, Peng Y, Ma Y (2009) Robust principal component analysis: exact recovery of corrupted low-rank matrices via convex optimization. In: Advances in neural information processing systems, pp 2080–2088Google Scholar
  49. Wu X,  Jia Y (2012) View-invariant action recognition using latent kernelized structural SVM. In: European conference on computer vision. Springer, pp 411–424Google Scholar
  50. Xia S, Shao M, Fu Y (2011) Kinship verification through transfer learning. IJCAI 22(3):2539Google Scholar
  51. Zhang W, Wang X, Tang X (2011) Coupled information-theoretic encoding for face photo-sketch recognition. In: CVPR. IEEE, pp 513–520Google Scholar
  52. Zhang Y, Shao M, Wong EK, Fu Y (2013) Random faces guided sparse many-to-one encoder for pose-invariant face recognition. In: IEEE international conference on computer vision. IEEE, pp 2416–2423Google Scholar
  53. Zhang F, Yang J, Tai Y, Tang J (2015) Double nuclear norm-based matrix decomposition for occluded image recovery and background modeling. IEEE Trans Image Process 24(6):1956–1966MathSciNetCrossRefGoogle Scholar
  54. Zhao H, Fu Y (2015) Dual-regularized multi-view outlier detection. In: IJCAI (2015), pp 4077–4083Google Scholar
  55. Zhao R, Ouyang W, Wang X (2013a) Person re-identification by salience matching. In: ICCV. IEEE, pp 2528–2535Google Scholar
  56. Zhao R, Ouyang W, Wang X (2013b) Unsupervised salience learning for person re-identification. In: CVPR. IEEE, pp 3586–3593Google Scholar
  57. Zhao R, Ouyang W, Wang X (2014) Learning mid-level filters for person re-identification. In: CVPR. IEEE, pp 144–151Google Scholar
  58. Zheng J, Jiang Z (2013) Learning view-invariant sparse representations for cross-view action recognition. In: IEEE international conference on computer vision. IEEE, pp 3176–3183Google Scholar
  59. Zheng W-S, Gong S, Xiang T (2013) Reidentification by relative distance comparison. TPAMI 35(3):653–668CrossRefGoogle Scholar
  60. Zhu Z, Luo P, Wang X, Tang X (2014) Multi-view perceptron: a deep model for learning face identity and view representations. In: Advances in neural information processing systems, pp 217–225Google 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