Abstract
Sequence classification is an important problem in computer vision, speech analysis or computational biology. This paper presents a new training strategy for the Hidden Conditional Random Field sequence classifier incorporating model and feature selection. The standard Lasso regularization employed in the estimation of model parameters is replaced by overlapping group-L1 regularization. Depending on the configuration of the overlapping groups, model selection, feature selection,or both are performed. The sequence classifiers trained in this way have better predictive performance. The application of the proposed method in a human action recognition task confirms that fact.
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References
Krogh, A., Brown, M., Mian, I.S., Sjolander, K., Haussler, D.: Hidden markov models in computational biology: Applications to protein modeling. Journal of Molecular Biology 235, 1501–1531 (1994)
Collins, M.: Discriminative training methods for hidden markov models: Theory and experiments with perceptron algorithms. In: Proceedings of the ACL 2002 Conference on Empirical Methods in Natural Language Processing, vol. 10, pp. 1–8. Association for Computational Linguistics (2002)
Yamato, J., Ohya, J., Ishii, K.: Recognizing human action in time-sequential images using hidden markov model. In: Proceedings of the 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 1992, pp. 379–385 (1992)
Bishop, C., et al.: Pattern recognition and machine learning, vol. 4. Springer, New York (2006)
Quattoni, A., Wang, S., Morency, L.P., Collins, M., Darrell, T.: Hidden conditional random fields. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 1848–1853 (2007)
Boyd, S., Vandenberghe, L.: Convex optimization. Cambridge University Press (2004)
Zhu, C., Byrd, R., Lu, P., Nocedal, J.: Algorithm 778: L-bfgs-b: Fortran subroutines for large-scale bound-constrained optimization. ACM Transactions on Mathematical Software (TOMS) 23, 550–560 (1997)
Ng, A.: Feature selection, l 1 vs. l 2 regularization, and rotational invariance. In: Proceedings of the Twenty-First International Conference on Machine Learning, p. 78. ACM (2004)
Huang, J., Zhang, T.: The benefit of group sparsity. The Annals of Statistics 38, 1978–2004 (2010)
Szabó, Z., Póczos, B., Lorincz, A.: Online group-structured dictionary learning. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp. 2865–2872. IEEE (2011)
Schmidt, M.: Graphical model structure learning with l1-regularization. PhD thesis, University of British Columbia (2010)
Bauschke, H., Lewis, A.: Dykstras algorithm with bregman projections: A convergence proof. Optimization 48, 409–427 (2000)
Wang, L., Suter, D.: Visual learning and recognition of sequential data manifolds with applications to human movement analysis. Computer Vision and Image Understanding 110, 153–172 (2008)
Gorelick, L., Blank, M., Shechtman, E., Irani, M., Basri, R.: Actions as space-time shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 2247–2253 (2007)
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Cilla, R., Patricio, M.A., Berlanga, A., Molina, J.M. (2013). Model and Feature Selection in Hidden Conditional Random Fields with Group Regularization. In: Pan, JS., Polycarpou, M.M., Woźniak, M., de Carvalho, A.C.P.L.F., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2013. Lecture Notes in Computer Science(), vol 8073. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40846-5_15
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DOI: https://doi.org/10.1007/978-3-642-40846-5_15
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