Multimedia Tools and Applications

, Volume 78, Issue 21, pp 30959–30973 | Cite as

Auto-weighted Mutli-view Sparse Reconstructive Embedding

  • Huibing Wang
  • Haohao Li
  • Xianping FuEmail author


With the development of multimedia era, multi-view data is generated in various fields. Contrast with those single-view data, multi-view data brings more useful information and should be carefully excavated. Therefore, it is essential to fully exploit the complementary information embedded in multiple views to enhance the performances of many tasks. Especially for those high-dimensional data, how to develop a multi-view dimension reduction algorithm to obtain the low-dimensional representations is of vital importance but chanllenging. In this paper, we propose a novel multi-view dimensional reduction algorithm named Auto-weighted Mutli-view Sparse Reconstructive Embedding (AMSRE) to deal with this problem. AMSRE fully exploits the sparse reconstructive correlations between features from multiple views. Furthermore, it is equipped with an auto-weighted technique to treat multiple views discriminatively according to their contributions. Various experiments have verified the excellent performances of the proposed AMSRE.


Multi-view Sparse representation Auto-weighted Mutli-view Sparse Reconstructive Embedding Dimension reduction 


Compliance with Ethical Standards

Conflict of interests

This study was funded by the National Natural Science Foundation of China Grant 61370142 and Grant 61272368, by the Fundamental Research Funds for the Central Universities Grant 3132016352, by the Fundamental Research of Ministry of Transport of P.R. China Grant 2015329225300. Huibing Wang, Haohao Li and Xianping Fu declare that they have no conflict of interest. Both Huibing Wang and Haohao Li contribute equally to this paper. This article does not contain any studies with human participants or animals performed by any of the authors.


  1. 1.
    Hu Q, Wang H, Li T, Shen C (2017) Deep cnns with spatially weighted pooling for fine-grained car recognition. IEEE Trans Intell Transp Syst 18(11):3147–3156CrossRefGoogle Scholar
  2. 2.
    Feng L, Wang H, Jin B, Li H, Xue M, Wang L (2018) Learning a distance metric by balancing kl-divergence for imbalanced datasets. IEEE Transactions on Systems, Man, and Cybernetics: SystemsGoogle Scholar
  3. 3.
    Wang Y, Wu L (2018) Beyond low-rank representations: Orthogonal clustering basis reconstruction with optimized graph structure for multi-view spectral clustering. Neural Netw 103:1–8CrossRefGoogle Scholar
  4. 4.
    Shen F, Shen C, Liu W, Shen HT (2015) Supervised discrete hashing. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 37–45Google Scholar
  5. 5.
    Wu L, Wang Y, Gao J, Li X (2018) Where-and-when to look: Deep siamese attention networks for video-based person re-identification. IEEE Transactions on MultimediaGoogle Scholar
  6. 6.
    Ahonen T, Hadid A, Pietikäinen M (2004) Face recognition with local binary patterns. In: European conference on computer vision, pp 469–481. SpringerGoogle Scholar
  7. 7.
    Ng PC, Henikoff S (2003) Sift: Predicting amino acid changes that affect protein function. Nucleic Acids Res 31(13):3812–3814CrossRefGoogle Scholar
  8. 8.
    Wang J, Yang J, Yu K, Lv F, Huang T, Gong Y (2010) Locality-constrained linear coding for image classification. In: 2010 IEEE conference on computer vision and pattern recognition (CVPR), pp 3360–3367. IEEEGoogle Scholar
  9. 9.
    Feng L, Yu L, Zhu H (2017) Spectral embedding-based multiview features fusion for content-based image retrieval. J Electron Imaging 26(5):053002CrossRefGoogle Scholar
  10. 10.
    Wu L, Wang Y, Shao L (2019) Cycle-consistent deep generative hashing for cross-modal retrieval. IEEE Trans Image Process 28(4):1602–1612MathSciNetCrossRefGoogle Scholar
  11. 11.
    Wang Y, Zhang W, Wu L, Lin X, Zhao X (2017) Unsupervised metric fusion over multiview data by graph random walk-based cross-view diffusion. IEEE Trans Neural Networks and Learning Systems 28(1):57–70CrossRefGoogle Scholar
  12. 12.
    Wang Y, Lin X, Wu L, Zhang W (2017) Effective multi-query expansions: Collaborative deep networks based feature learning for robust landmark retrieval. IEEE Trans Image Process PP(99):1–1Google Scholar
  13. 13.
    Kumar A, Rai P, Daume H (2011) Co-regularized multi-view spectral clustering. In: Advances in neural information processing systems, pp 1413–1421Google Scholar
  14. 14.
    Xia T, Tao D, Mei T, Zhang Y (2010) Multiview spectral embedding. IEEE Trans Syst Man Cybern Part B Cybern 40(6):1438–1446CrossRefGoogle Scholar
  15. 15.
    Wang Y, Wu L, Lin X, Gao J (2018) Multiview spectral clustering via structured low-rank matrix factorization. IEEE Transactions on Neural Networks and Learning SystemsGoogle Scholar
  16. 16.
    Wang H, Feng L, Yu L, Zhang J (2016) Multi-view sparsity preserving projection for dimension reduction. Neurocomputing 216:286–295CrossRefGoogle Scholar
  17. 17.
    Kan M, Shan S, Zhang H, Lao S, Chen X (2016) Multi-view discriminant analysis. IEEE Trans Pattern Anal Mach Intell 38(1):188–194CrossRefGoogle Scholar
  18. 18.
    Mika S, Ratsch G, Weston J, Scholkopf B, Mullers K-R (1999) Fisher discriminant analysis with kernels. In: Neural networks for signal processing IX, 1999. Proceedings of the IEEE signal processing society workshop, pp 41–48. IEEEGoogle Scholar
  19. 19.
    Luo Y, Tao D, Ramamohanarao K, Xu C, Wen Y (2015) Tensor canonical correlation analysis for multi-view dimension reduction. IEEE Trans Knowl Data Eng 27(11):3111–3124CrossRefGoogle Scholar
  20. 20.
    Wang Y, Lin X, Wu L, Zhang W, Zhang Q (2015) Lbmch: Learning bridging mapping for cross-modal hashing. In: Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval, pp 999–1002. ACMGoogle Scholar
  21. 21.
    Wu L, Wang Y, Gao J, Li X (2018) Deep adaptive feature embedding with local sample distributions for person re-identification. Pattern Recogn 73:275–288CrossRefGoogle Scholar
  22. 22.
    Shen C, Kim J, Wang L (2011) A scalable dual approach to semidefinite metric learning. In: 2011 IEEE conference on computer vision and pattern recognition (CVPR), pp 2601–2608. IEEEGoogle Scholar
  23. 23.
    Wang H, Feng L, Zhang J, Liu Y (2016) Semantic discriminative metric learning for image similarity measurement. IEEE Trans Multimedia 18(8):1579–1589CrossRefGoogle Scholar
  24. 24.
    Liu C, Feng L, Wang H, Wu B (2018) Face alignment via multi-regressors collaborative optimization. IEEE AccessGoogle Scholar
  25. 25.
    Deng R, Shen C, Liu S, Wang H, Liu X (2018) Learning to predict crisp boundaries. arXiv:1807.10097
  26. 26.
    Wu L, Wang Y, Shao L, Wang M (2019) 3d personvlad: Learning deep global representations for video-based person re-identification. IEEE Transactions on Neural Networks and Learning SystemsGoogle Scholar
  27. 27.
    Agarwal M, Agrawal H, Jain N, Kumar M (2009) Face recognition using principle component analysis, eigenface and neural network. In: Wseas international conference on sensorsGoogle Scholar
  28. 28.
    He X, Niyogi P (2004) Locality preserving projections. In: Advances in neural information processing systems, pp 153–160Google Scholar
  29. 29.
    He X, Cai D, Yan S, Zhang H-J (2005) Neighborhood preserving embedding. In: 10th IEEE International Conference on Computer vision, 2015. ICCV 2005, vol 2, pp 1208–1213. IEEEGoogle Scholar
  30. 30.
    Qiao L, Chen S, Tan X (2010) Sparsity preserving projections with applications to face recognition. Pattern Recogn 43(1):331–341CrossRefGoogle Scholar
  31. 31.
    Wu L, Wang Y, Li X, Gao J (2018) What-and-where to match: Deep spatially multiplicative integration networks for person re-identification. Pattern Recogn 76:727–738CrossRefGoogle Scholar
  32. 32.
    Wang Y, Zhang W, Wu L, Lin X, Fang M, Pan S (2016) Iterative views agreement: An iterative low-rank based structured optimization method to multi-view spectral clustering. In: International joint conference on artificial intelligence, pp 2153–2159Google Scholar
  33. 33.
    Belkin M, Niyogi P (2002) Laplacian eigenmaps and spectral techniques for embedding and clustering. In: Advances in neural information processing systems, pp 585–591Google Scholar
  34. 34.
    Wang Y, Lin X, Wu L, Zhang W, Zhang Q, Huang X (2015) Robust subspace clustering for multi-view data by exploiting correlation consensus. IEEE Trans Image Process 24(11):3939–3949MathSciNetCrossRefGoogle Scholar
  35. 35.
    Hardoon DR, Szedmak S, Shawe-Taylor J (2004) Canonical correlation analysis: An overview with application to learning methods. Neural Comput 16(12):2639–2664CrossRefGoogle Scholar
  36. 36.
    Denoeux T (1995) A k-nearest neighbor classification rule based on dempster-shafer theory. IEEE Trans Syst Man Cybern 25(5):804–813CrossRefGoogle Scholar
  37. 37.
    Fant E, Casady W, Goh D, Siebenmorgen T (1994) Grey-scale intensity as a potential measurement for degree of rice milling. J Agric Eng Res 58(2):89–98CrossRefGoogle Scholar
  38. 38.
    Gao X, Xiao B, Tao D, Li X (2008) Image categorization: Graph edit distance+ edge direction histogram. Pattern Recogn 41(10):3179–3191CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Information and Science TechnologyDalian Maritime UniversityDalianChina
  2. 2.School of Mathematical SciencesDalian University of TechnologyDalianChina

Personalised recommendations