Soft Computing

, Volume 21, Issue 18, pp 5425–5441 | Cite as

Cognitive gravitation model-based relative transformation for classification

  • Yaxin Sun
  • Guihua Wen
Methodologies and Application


The classifiers based on the relative transformation have good effectiveness in classification on the noisy, sparse and high-dimensional data. However, the relative transformation only simply transforms features from the original space to the relative space by Euclidean distances. It still ignores many other human perceptions. For example, to identify an object, human may find the difference among similar objects and adjust the recognition results according to the densities of classes. To simulate these two human perceptions, this paper first modifies the cognitive gravity model with a new way to estimate mass and then Gaussian transformation, and then applies the modified model to redefine the relative transformation, denoted as CGRT. Subsequently, a new classifier is designed, which utilizes CGRT to transform the original space to the relative space in which the classification is performed. The conducted experiments on challenging benchmark datasets validate the CGRT and the designed classifier.


Cognitive laws Classification Feature transformation Face recognition 



This work was supported by China National Science Foundation under Grants 60973083, 61273363, State Key Laboratory of Brain and Cognitive Science under grants 08B12.

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflicts of interest.


  1. Bergman TJ, Beehner JC, Cheney DL, Seyfarth RM (2003) Hierarchical classification by rank and Kinship in Baboons. Science 302(5648):1234–1236CrossRefGoogle Scholar
  2. Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Data Mining Knowl Discov 2(2):121–167CrossRefGoogle Scholar
  3. Cai X, Wen G, Wei J, Li J, Yu Z (2014) Perceptual relativity-based semi-supervised dimensionality reduction algorithm. Appl Soft Comput 16:112–123CrossRefGoogle Scholar
  4. Chang C-C, Lin C-J (2011) LIBSVM—a library for support vector machines. ACM Trans Intell Syst Technol 2(3):1–27CrossRefGoogle Scholar
  5. Chen J, Yi Z (2014) Sparse representation for face recognition by discriminative low-rank matrix recovery. J Vis Commun Image Represent 25(5), 763–773Google Scholar
  6. Cover T, Hart P (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13(1):21–27CrossRefzbMATHGoogle Scholar
  7. Desolneux A, Moisan L, Morel JM (2002) Computational gestalts and perception thresholds. J Physiol Paris 97(2–3):311–324Google Scholar
  8. Feng Z, Yang M, Zhang L, Liu Y, Zhang D (2014) Joint discriminative dimensionality reduction and dictionary learning for face recognition. In: Proceedings of the international conference on computer vision and pattern recognition (CVPR), ColumbusGoogle Scholar
  9. He x, Cai D, Yan S, Zhang HJ (2005) Neighborhood preserving embedding. In: Proceedings of the international conference on computer vision (ICCV), Beijing, pp 1208–1213Google Scholar
  10. He X, Niyogi P (2003) Locality preserving projections. In: Proceedings of the advances in neural information processing systems (NIPS), Vancouver, pp 585–591Google Scholar
  11. Huang H, Li J, Liu J (2012) Enhanced semi-supervised local Fisher discriminant analysis for face recognition. Future Gener Comput Syst 28(1):244–253CrossRefGoogle Scholar
  12. Huang G, Song S, Gupta JND, Wu C (2013) A second order cone programming approach for semi-supervised learning. Pattern Recognit 46(12):3548–3558CrossRefzbMATHGoogle Scholar
  13. Jia WEI, Hong PENG (2008) Local and global preserving based semi-supervised dimensionality reduction method. J Softw 19(11):2833–3842Google Scholar
  14. Jiang Zhuolin, Lin Zhe, Davis LS (2013) Label consistent K-SVD: learning a discriminative dictionary for recognition. IEEE Trans Pattern Anal Mach Intell 35(11):2651–2664CrossRefGoogle Scholar
  15. Mairal J, Bach F, Ponce J, Sapiro G (2009) Online dictionary learning for sparse coding. In: Proceedings of the international conference on machine learning (ICML), pp 689–696Google Scholar
  16. Martinez A, Benavente R (1998) The AR face database. CVC technical report 24Google Scholar
  17. Martinez AM, Kak AC (2001) PCA versus LDA. Trans Pattern Anal Mach Intell 23(2):228–233CrossRefGoogle Scholar
  18. Nasiri JA, Charkari NM, Mozafari K (2014) Energy-based model of least squares twin Support Vector Machines for human action recognition. Signal Process 104:248–257CrossRefGoogle Scholar
  19. Nene SA, Nayar SK, Murase H (1996) Columbia object image library (COIL-20). Technical report CUCS-005-96Google Scholar
  20. Ogiela L, Ogiela MR (2011) Semantic analysis processes in advanced pattern understanding systems. international conference on advanced science and technology, Jeju Island, pp 26–30Google Scholar
  21. Ogiela L, Ogiela MR (2014) Cognitive systems for intelligent business information management in cognitive economy. Int J Inf Manag 34(6):751–760CrossRefzbMATHGoogle Scholar
  22. Ogiela L, Ogiela MR (2014) Cognitive systems and bio-inspired computing in Homeland security. J Netw Comput Appl 38:34–42CrossRefGoogle Scholar
  23. Pan Rotation Face Database,
  24. Peng L, Yang B, Chen Y, Abraham A (2009) Data gravitation based classification. Inf Sci 179(6):809–819CrossRefzbMATHGoogle Scholar
  25. Samaria FS, Harter AC (1994) Parameterization of a stochastic model for human. In: Proceedings of the IEEE workshop on applications of computer vision (ACV), Sarasota, pp 138–142Google Scholar
  26. Shang F, Jiao LC, Liu Y (2013) Semi-supervised learning with nuclear norm regularization. Pattern Recognit 46(8):2323–2336CrossRefzbMATHGoogle Scholar
  27. Sim T, Baker S, Bsat M, The CMU Pose, Illumination, and expression (PIE) database. In: Proceedings of the IEEE international conference on automatic face and gesture recognition (AFGR), Washington, pp. 46–51Google Scholar
  28. Sujay Raghavendra N, Paresh Chandra D (2014) Support vector machine applications in the field of hydrology: a review. Appl Soft Comput 19:372–386Google Scholar
  29. Wang Q, Yuen PC, Feng G (2013) Semi-supervised metric learning via topology preserving multiple semi-supervised assumptions. Pattern Recognit 46(9):2576–2578CrossRefzbMATHGoogle Scholar
  30. Wen G, Jiang L, Wen J (2009) Local relative transformation with application to isometric embedding. Pattern Recognit Lett 30(3):203–211CrossRefGoogle Scholar
  31. Wen G (2009) Relative transformation-based neighborhood optimization for isometric embedding. Neurocomputing 72(4–6):1205–1213CrossRefGoogle Scholar
  32. Wen G, Jiang L, Wen J, Wei J, Yu Z (2012) Perceptual relativity-based local hyperplane classification. Neurocomputing 97(15):155–163CrossRefGoogle Scholar
  33. Wen G, Wei J, Wang J, Zhou T, Chen L (2013) Cognitive gravitation model for classification on small noisy data. Neurocomputing 118(22):245–252CrossRefGoogle Scholar
  34. Wen G, Jiang L (2011) Relative local mean classifier with optimized decision rule, international conference on computational intelligence and security (CIS), Hainan, pp 477–481Google Scholar
  35. Wen G, Wei J, Yu Z, Wen J, Jiang L (2011) Relative nearest neighbors for classification, international conference on machine learning and cybernetics (ICMLC), Guilin, pp 773–778Google Scholar
  36. Wright J, Yang AY, Ganesh A, Sastry SS, Ma Y (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31(2):210–227CrossRefGoogle Scholar
  37. Yang M, Zhang L, Feng X, Zhang D (2011) Fisher discrimination dictionary learning for sparse representation. In: Proceedings of the international conference on computer vision (ICCV), Washington, pp 543–550Google Scholar
  38. Zhao M, Zhang Z, Chow TWS (2012) Trace ratio criterion based generalized discriminative learning for semi-supervised dimensionality reduction. Pattern Recognit 45(4):1482–1499CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Jiaxing UniversityJiaxingChina
  2. 2.South China University of TechnologyGuangzhouChina

Personalised recommendations