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mPadal: a joint local-and-global multi-view feature selection method for activity recognition

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Abstract

The selection of multi-view features plays an important role for classifying multi-view data, especially the data with high dimension. In this paper, a novel multi-view feature selection method via joint local pattern-discrimination and global label-relevance analysis (mPadal) is proposed. Different from the previous methods which globally select the multi-view features directly via view-level analysis, the proposed mPadal employs a new joint local-and-global way. In the local selection phase, the pattern-discriminative features will be first selected by considering the local neighbor structure of the most discriminative patterns. In the global selection phase, the features with the topmost label-relevance, which can well separate different classes in the current view, are selected. Finally, the two parts selected are combined to form the final features. Experimental results show that compared with several baseline methods in publicly available activity recognition dataset IXMAS, mPadal performs the best in terms of the highest accuracy, precision, recall and F1 score. Moreover, the features selected by mPadal are highly complementary among views for classification, which is able to improve the classification performance according to previous theoretical studies.

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Notes

  1. 1 http://4drepository.inrialpes.fr/public/viewgroup/6

  2. 2 F1 score is also called F-measure in other articles.

References

  1. Ando R, Zhang T (2007) Two-view feature generation model for semi-supervised learning. In: Proceedings of the 24th international conference on machine learning. ACM, pp 25–32

  2. Blum A, Mitchell T (1998) Combining labeled and unlabeled data with co-training. In: Proceedings of the eleventh annual conference on computational learning theory. ACM, pp 92–100

  3. Burges C (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 2(2):121–167

    Article  Google Scholar 

  4. Byrd R, Lu P, Nocedal J, Zhu C (1995) A limited memory algorithm for bound constrained optimization. SIAM J Sci Comput 16(5):1190–1208

    Article  MathSciNet  MATH  Google Scholar 

  5. Cao L, Ou Y, Yu P (2012) Coupled behavior analysis with applications. IEEE Trans Knowl Data Eng 24(8):1378–1392

    Article  Google Scholar 

  6. Chen N, Zhu J, Sun F, Xing E (2012) Large-margin predictive latent subspace learning for multi-view data analysis. IEEE Trans Pattern Anal Mach Intell, p. preprint

  7. Cheung K, Baker S, Kanade T (2003) Shape-from-silhouette of articulated objects and its use for human body kinematics estimation and motion capture. In: 2003 IEEE conference on computer vision and pattern recognition (CVPR), vol 1. IEEE, pp 77–84

  8. Dhillon I, Mallela S, Kumar R (2003) A divisive information theoretic feature clustering algorithm for text classification. J Mach Learn Res 3:1265–1287

    MathSciNet  MATH  Google Scholar 

  9. Feng Y, Xiao J, Zhuang Y, Liu X (2012) Adaptive unsupervised multi-view feature selection for visual concept recognition. In: Computer vision–ACCV. Springer, pp 343–357

  10. Gondal I, Murshed M, et al. (2011) On dynamic scene geometry for view-invariant action matching. In: 2011 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 3305–3312

  11. Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182

    MATH  Google Scholar 

  12. Guyon I, Weston J, Barnhill S, Vapnik V (2002) Gene selection for cancer classification using support vector machines. Mach Learn 46(1):389–422

    Article  MATH  Google Scholar 

  13. He X, Cai D, Niyogi P (2006) Laplacian score for feature selection. Adv Neural Inf Process Syst 18:507

    Google Scholar 

  14. Idris A, Khan A, Lee Y (2013) Intelligent churn prediction in telecom: employing mrmr feature selection and rotboost based ensemble classification. Appl Intell 39(3):659–672

    Article  Google Scholar 

  15. Kononenko I (1994) Estimating attributes: analysis and extensions of relief. In: Machine learning: ECML-94. Springer, pp 171–182

  16. Lee S, Park Y, d’Auriol B (2012) A novel feature selection method based on normalized mutual information. Appl Intell 37(1):100–120

    Article  Google Scholar 

  17. Li G, Chang K, Hoi S (2011) Multi-view semi-supervised learning with consensus. IEEE Trans Knowl Data Eng (99), preprint

  18. Li R, Zickler T (2012) Discriminative virtual views for cross-view action recognition. In: 2012 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 2855–2862

  19. Liu J, Ali S, Shah M (2008) Recognizing human actions using multiple features. In: 2008 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 1–8

  20. Liu J, Shah M (2008) Learning human actions via information maximization. In: 2008 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 1–8

  21. Liu J, Shah M, Kuipers B, Savarese S (2011) Cross-view action recognition via view knowledge transfer. In: 2011 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 3209–3216

  22. Liu X, De Lathauwer L, Ji S, Glänzel W, De Moor B (2011) Multi-view partitioning via tensor methods. IEEE Trans Knowl Data Eng

  23. Lv F, Nevatia R (2007) Single view human action recognition using key pose matching and viterbi path searching. In: 2007 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 1–8

  24. Maaten L, Postma E, Herik H (2009) Dimensionality reduction: a comparative review. J Mach Learn Res 10:1–41

    Google Scholar 

  25. Moustakidis S, Theocharis J (2010) Svm-fuzcoc: a novel svm-based feature selection method using a fuzzy complementary criterion. Pattern Recog 43(11):3712–3729

    Article  MATH  Google Scholar 

  26. Owusu E, Zhan Y, Mao Q (2014) An SVM-AdaBoost facial expression recognition system. Appl Intell 40(3):536–545

    Article  Google Scholar 

  27. Peng H, Long F, Ding C (2005) Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27(8):1226–1238

    Article  Google Scholar 

  28. Poppe R (2010) A survey on vision-based human action recognition. Image Vis Comput 28(6):976–990

    Article  Google Scholar 

  29. Ramagiri S, Kavi R, Kulathumani V (2011) Real-time multi-view human action recognition using a wireless camera network. 2011 fifth ACM/IEEE international conference on distributed smart cameras (ICDSC). IEEE, pp 1–6

  30. Reddy K, Liu J, Shah M (2009) Incremental action recognition using feature-tree. In: 2009 IEEE 12th international conference on computer vision. IEEE, pp 1010–1017

  31. Roberts R, Potthast C, Dellaert F (2009) Learning general optical flow subspaces for egomotion estimation and detection of motion anomalies. In: 2009 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 57–64

  32. Sakar C, Kursun O, Gurgen F (2013) Ensemble canonical correlation analysis. Appl Intell 40(2):291–304

    Article  Google Scholar 

  33. Sheikhan M (2014) Generation of suprasegmental information for speech using a recurrent neural network and binary gravitational search algorithm for feature selection. Appl Intell 40(4):772– 790

    Article  Google Scholar 

  34. 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):113–126

    Article  Google Scholar 

  35. Shi Y, Gao Y, Wang R, Zhang Y, Wang D (2013) Transductive cost-sensitive lung cancer image classification. Appl Intell 38(1):16–28

    Article  Google Scholar 

  36. Shi Y, Gao Y, Yang Y, Zhang Y, Wang D (2013) Multi-modal sparse representation-based classification for lung needle biopsy images. IEEE Trans Biomed Eng 60(10):2675–2685

    Article  Google Scholar 

  37. Shi Y, Liao S, Gao Y, Zhang D, Gao Y, Shen D (2013) Prostate segmentation in CT images via spatial-constrained transductive Lasso. In: 2013 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 2227–2234

  38. Sindhwani V, Rosenberg D (2008) An rkhs for multi-view learning and manifold co-regularization. In: Proceedings of the 25th international conference on machine learning. ACM, pp 976–983

  39. Tang J, Hu X, Gao H, Liu H (2013) Unsupervised feature selection for multi-view data in social media. In: SIAM international conference on data mining, pp 270–278

  40. Tibshirani R (1996) Regression shrinkage and selection via the Lasso. J R Stat Soc: Ser B (Methodological) 267–288

  41. Tran D, Sorokin A (2008) Human activity recognition with metric learning. Comput Visn–ECCV 2008:548–561

    Google Scholar 

  42. Tzevanidis K, Argyros A (2011) Unsupervised learning of background modeling parameters in multicamera systems. Comput Vision Image Underst 115(1):105–116

    Article  Google Scholar 

  43. Viola P, Jones M (2004) Robust real-time face detection. Int J Comput Vis 57(2):137–154

    Article  Google Scholar 

  44. Wang H, Nie F, Huang H (2013) Multi-view clustering and feature learning via structured sparsity. In: Proceedings of the 30th international conference on machine learning (ICML-13). ACM, pp 352–360

  45. Wang H, Ullah M, Kläser A, Laptev I, Schmid C (2009) Evaluation of local spatio-temporal features for action recognition. University of Central Florida, USA. Citeseer

  46. Weinland D, Boyer E, Ronfard R (2007) Action recognition from arbitrary views using 3d exemplars. In: 2007 IEEE 11th International Conference on Computer Vision. IEEE, pp 1–7

  47. Weinland D, Özuysal M, Fua P (2010) Making action recognition robust to occlusions and viewpoint changes. In: Computer vision–ECCV 2010. Springer, pp 635–648

  48. Weinland D, Ronfard R, Boyer E (2006) Free viewpoint action recognition using motion history volumes. Comput Vis Image Underst 104(2):249–257

    Article  Google Scholar 

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

    Article  Google Scholar 

  50. Xing E, Yan R, Hauptmann A (2005) Mining associated text and images with dual-wing harmoniums. In: Proceedings of the 21th international conference on uncertainty in artificial intelligence. AUAI

  51. Zhang Z, Wang C, Xiao B, Zhou W, Liu S, Shi C (2013) Cross-view action recognition via a continuous virtual path. In: 2013 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 2690–2697

  52. Zhou Z, Li M (2005) Tri-training: Exploiting unlabeled data using three classifiers. IEEE Trans Knowl Data Eng 17 (11): 1529–1541

    Article  Google Scholar 

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Acknowledgments

We would like to acknowledge the support for this work from the National Science Foundation of China (Grant Nos. 61035003, 61175042, 61321491, 61305068), the 973 Program of Jiangsu, China (Grant No. BK2011005), Jiangsu NSF (Grant No. BK20130581), the Program for New Century Excellent Talents in University (NCET-10-0476), Jiangsu Clinical Medicine Special Program (No.BL2013033), and the Graduate Research Innovation Program of Jiangsu, China (CXZZ13_0055). Also, this work was partially supported by Collaborative Innovation Center of Novel Software Technology and Industrialization.

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Authors and Affiliations

  1. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China

    Wanqi Yang, Yang Gao & Yinghuan Shi

  2. Advanced Analytics Institute, University of Technology Sydney, Sydney, Australia

    Longbing Cao

  3. School of Computer Science and Technology, Nanjing Normal University, Nanjing, China

    Ming Yang

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  1. Wanqi Yang
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  2. Yang Gao
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  3. Longbing Cao
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Correspondence to Yang Gao.

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Yang, W., Gao, Y., Cao, L. et al. mPadal: a joint local-and-global multi-view feature selection method for activity recognition. Appl Intell 41, 776–790 (2014). https://doi.org/10.1007/s10489-014-0566-5

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