Abstract
This paper proposes a novel and Robust Parametric Twin Support Vector Machine (RPTWSVM) classifier to deal with the heteroscedastic noise present in the human activity recognition framework. Unlike Par-\(\nu \)-SVM, RPTWSVM proposes two optimization problems where each one of them deals with the structural information of the corresponding class in order to control the effect of heteroscedastic noise on the generalization ability of the classifier. Further, the hyperplanes so obtained adjust themselves in order to maximize the parametric insensitive margin. The efficacy of the proposed framework has been evaluated on standard UCI benchmark datasets. Moreover, we investigate the performance of RPTWSVM on human activity recognition problem. The effectiveness and practicability of the proposed algorithm have been supported with the help of experimental results.
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References
Cheng, Guangchun, Yiwen Wan, Abdullah N. Saudagar, Kamesh Namuduri, Bill P. Buckles Advances in Human Action Recognition: A Survey. arXiv preprint arXiv:1501.05964 (2015).
Aggarwal, J. K. and L. Xia, Human activity recognition from 3D data: a review, Pattern Recognition Letters, vol. 48, pp. 70–80, (2014).
Tran, Du, and Alexander Sorokin. Human activity recognition with metric learning. Computer VisionECCV 2008. Springer Berlin Heidelberg, 548–561, (2008).
Manosha Chathuramali, K. G., and Ranga Rodrigo. Faster human activity recognition with SVM. Advances in ICT for Emerging Regions (ICTer), 2012 International Conference on. IEEE, (2012).
Jayadeva, Khemchandani, R., and Suresh Chandra. Twin support vector machines for pattern classification. Pattern Analysis and Machine Intelligence, IEEE Transactions on 29.5: 905–910, (2007).
Nasiri, Jalal A., Nasrollah Moghadam Charkari, and Kourosh Mozafari. Energy-based model of least squares twin Support Vector Machines for human action recognition. Signal Processing 104: 248–257 (2014).
Wang, Zhen, Yuan-Hai Shao, and Tie-Ru Wu. Proximal parametric-margin support vector classifier and its applications. Neural Computing and Applications 24.3-4: 755–764, (2014).
Hao, Pei-Yi. New support vector algorithms with parametric insensitive/margin model. Neural Networks 23.1: 60–73, (2010).
Schlkopf, Bernhard, et al. New support vector algorithms. Neural computation 12.5: 1207–1245, (2000).
Peng, Xinjun. TPMSVM: a novel twin parametric-margin support vector machine for pattern recognition. Pattern Recognition 44.10: 2678–2692, (2011).
Blank, Moshe, et al. Actions as space-time shapes. Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on. Vol. 2. IEEE, (2005).
Blake, Catherine, and Christopher J. Merz. UCI Repository of machine learning databases, (1998).
Hsu, Chih-Wei, Chih-Chung Chang, and Chih-Jen Lin. A practical guide to support vector classification, (2003).
Duda, Richard O., Peter E. Hart, and David G. Stork, Pattern classification, John Wiley & Sons, (2012).
Lucas, Bruce D., and Takeo Kanade. An iterative image registration technique with an application to stereo vision. IJCAI. Vol. 81 (1981).
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Khemchandani, R., Sharma, S. (2017). Robust Parametric Twin Support Vector Machine and Its Application in Human Activity Recognition. In: Raman, B., Kumar, S., Roy, P., Sen, D. (eds) Proceedings of International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 459. Springer, Singapore. https://doi.org/10.1007/978-981-10-2104-6_18
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DOI: https://doi.org/10.1007/978-981-10-2104-6_18
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