Skip to main content
SpringerLink
Log in

Structure Preserving Sparse Coding for Data Representation

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

Sparse coding methods have shown the superiority in data representation. However, traditional sparse coding methods cannot explore the manifold structure embedded in data. To alleviate this problem, a novel method, called Structure Preserving Sparse Coding (SPSC), is proposed for data representation. SPSC imposes both local affinity and distant repulsion constraints on the model of sparse coding. Therefore, the proposed SPSC method can effectively exploit the structure information of high dimensional data. Beside, an efficient optimization scheme for our proposed SPSC method is developed, and the convergence analysis on three datasets are presented. Extensive experiments on several benchmark datasets have shown the superior performance of our proposed method compared with other state-of-the-art methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Zhao W, Chellappa R, Phillips PJ et al (2003) Face recognition: a literature survey. ACM Comput Surv (CSUR) 35(4):399–458

    Article  Google Scholar 

  2. Turk M, Pentland A (1991) Eigenfaces for recognition. J Cognit Neurosci 3(1):71–86

    Article  Google Scholar 

  3. Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720

    Article  Google Scholar 

  4. Lee JM, Yoo CK, Choi SW et al (2004) Nonlinear process monitoring using kernel principal component analysis. Chem Eng Sci 59(1):223–234

    Article  Google Scholar 

  5. Liu T, Tao D, Xu D (2016) Dimensionality-dependent generalization bounds for k-dimensional coding schemes. Neural Comput 28(10):2213–2249

    Article  Google Scholar 

  6. Shu Z, Fan H, Huang P et al (2017) Multiple Laplacian graph regularized low rank representation with application to image representation. IET Image Process 11(6):370–378

    Article  Google Scholar 

  7. Belkin M, Niyogi P (2003) Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput. 15(6):1373–1396

    Article  Google Scholar 

  8. Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323–2326

    Article  Google Scholar 

  9. Tenenbaum JB, de Silva V, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290(5500):2319–2323

    Article  Google Scholar 

  10. Lee D, Seung H (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401:788–791

    Article  Google Scholar 

  11. Cai D, He X, Han J (2011) Locally consistent concept factorization for document clustering. IEEE Trans Knowl Data Eng 23(6):902–913

    Article  Google Scholar 

  12. Liu T, Gong M, Tao D (2017) Large-cone nonnegative matrix factorization. IEEE Trans Neural Netw Learn Syst 28(9):2129–2142

    MathSciNet  Google Scholar 

  13. Shu Z, Zhao C, Huang P (2015) Local regularization concept factorization and its semi-supervised extension for image representation. Neurocomputing 152(22):1–12

    Article  Google Scholar 

  14. Li Z, Liu J, Lu H (2013) Structure preserving non-negative matrix factorization for dimensionality reduction. Comput Vis Image Underst 117(9):1175–1189

    Article  Google Scholar 

  15. Liu T, Tao D (2016) On the performance of manhattan nonnegative matrix factorization. IEEE Trans Neural Netw Learn Syst 27(9):1851–1863

    Article  MathSciNet  Google Scholar 

  16. Candès EJ, Romberg J, Tao T (2006) Stable signal recovery from incomplete and inaccurate measurements. Commun Pure Appl Math 59(8):1207–1223

    Article  MathSciNet  Google Scholar 

  17. Donoho DL (2006) Compressed sensing. IEEE Inf Theory Soc 52(4):1289–1306

    Article  MathSciNet  Google Scholar 

  18. Qian Y, Jia S, Zhou J et al (2011) Hyperspectral unmixing via L-1/2 sparsity-constrained nonnegative matrix factorization. IEEE Trans Geosci Remote Sens 49(11):4282–4297

    Article  Google Scholar 

  19. Zou H, Hastie T, Tibshirani R (2006) Sparse principal component analysis. J Comput Gr Stat 15(2):265–286

    Article  MathSciNet  Google Scholar 

  20. Deng J, Zhang Z, Marchi E et al. (2013) Sparse autoencoder-based feature transfer learning for speech emotion recognition, In: IEEE affective computing and intelligent interaction, pp 511–516

  21. Wright J, Yang A, Sastry S et al (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31(2):210–227

    Article  Google Scholar 

  22. Zheng M, Bu J, Chen C et al (2011) Graph regularized sparse coding for image representation. IEEE Trans Image Process 20(5):1327–1336

    Article  MathSciNet  Google Scholar 

  23. Liu W, Zha Z, Wang Y et al (2016) p-Laplacian regularized sparse coding for human activity recognition. IEEE Trans Ind Electron 63(8):5120–5129

    Google Scholar 

  24. Hong C, Yu J, Tao D et al (2015) Image-based 3d human pose recovery by multi-view locality sensitive sparse retrieval. IEEE Trans Ind Electron 62(6):3742–3751

    Google Scholar 

  25. Yu J, Rui Y, Tao D (2014) Click prediction for web image reranking using multimodal sparse coding. IEEE Trans Image Process 23(5):2019–2032

    Article  MathSciNet  Google Scholar 

  26. Geng B, Tao D, Xu C et al (2012) Ensemble manifold regularization. IEEE Trans Pattern Anal Mach Intell 34(6):1227–1233

    Article  Google Scholar 

  27. Jin T, Yu Z, Li L et al (2015) Multiple graph regularized sparse coding and multiple hypergraph regularized sparse coding for image representation. Neurocomputing 154:245–256

    Article  Google Scholar 

  28. Yu J, Hong R, Wang M et al (2014) Image clustering based on sparse patch alignment framework. Pattern Recognit 47(11):3512–3519

    Article  Google Scholar 

  29. Shu Z, Zhou J, Huang P et al (2016) Local and global regularized sparse coding for data representation. Neurocomputing 198(29):188–197

    Article  Google Scholar 

  30. Carreira-Perpinan MA (2010) The elastic embedding algorithm for dimensionality reduction. ICML 10:167–174

    Google Scholar 

  31. Donoho DL (2006) For most large underdetermined systems of equations, the minimal 1-norm near-solution approximates the sparsest near-solution. Commun Pure Appl Math 59(6):797–829

    Article  MathSciNet  Google Scholar 

  32. Kim SJ, Koh K, Lustig M, Boyd S, Gorinevsky D (2007) A method for large-scale l1-regularized least squares. IEEE J Sel Top Signal Process 1:606–617

    Article  Google Scholar 

  33. Cands E, Romberg J (2006) \(l_{1}\)-magic: a collection of matlab routines for solving the convex optimization programs central to compressive sampling. www.acm.caltech.edu/l1magic/

  34. Saunders M (2002) PDCO: Primal-dual interior method for convex objectives. http://www.stanford.edu/group/SOL/ software/pdco.html

  35. Lee H, Battle A, Raina R et al (2007) Efficient sparse coding algorithms. Proc Adv Neural Inf Process Syst 20:801–808

    Google Scholar 

Download references

Acknowledgements

This work was supported by the Grants of the National Natural Science Foundation of China [Grant Nos. 61472166, 61603159, 61672265, 61373055, 61503195], Natural Science Foundation of Jiangsu Province [Grant No. BK20160293], Jiangsu Key Laboratory of Image and Video Understanding for Social Safety (Nanjing University of Science and Technology) [Grant No. 30916014107], China Postdoctoral Science Foundation [Grant No. 2017M611695], and Jiangsu Province Postdoctoral Science Foundation [Grant No. 1701094B].

Author information

Authors and Affiliations

  1. School of Computer Engineering, Jiangsu University of Technology, Changzhou, China

    Zhenqiu Shu

  2. School of IoT Engineering, Jiangnan University, Wuxi, China

    Zhenqiu Shu, Xiao-jun Wu & Cong Hu

  3. Jiangsu Key Laboratory of Image and Video Understanding for Social Safety, Nanjing University of Science and Technology, Nanjing, China

    Zhenqiu Shu

Authors
  1. Zhenqiu Shu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiao-jun Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Cong Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiao-jun Wu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shu, Z., Wu, Xj. & Hu, C. Structure Preserving Sparse Coding for Data Representation. Neural Process Lett 48, 1705–1719 (2018). https://doi.org/10.1007/s11063-018-9796-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-018-9796-6

Keywords

Search

Navigation