Discriminative learning for dynamic state prediction

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 31, Issue 10, 2009, Pages 1847-1861

  Kim, Minyoung ^a   Pavlovic, Vladimir ^a  

^a RUTGERS UNIVERSITY   (United States)

Author keywords

Conditional random fields; Discriminative models and learning; Dynamic state prediction; State space models

Indexed keywords

CONDITIONAL MODELS; CONDITIONAL RANDOM FIELD; CONDITIONAL RANDOM FIELDS; DISCRIMINATIVE APPROACH; DISCRIMINATIVE LEARNING; DISCRIMINATIVE MODELS AND LEARNING; DISCRIMINATIVE TRAINING; DYNAMIC STATE; DYNAMIC STATE PREDICTION; DYNAMIC SYSTEMS; HUMAN MOTIONS; INTEGRABILITY CONSTRAINT; JOINT MODELING; PARAMETER LEARNING; PARAMETER SPACES; PREDICTION ACCURACY; PROBABILISTIC MODELS; PROBLEM DOMAIN; STATE-OF-THE-ART METHODS; STATE-SPACE MODELS;

CONTENT BASED RETRIEVAL; DYNAMICAL SYSTEMS; EDUCATION; HIDDEN MARKOV MODELS; LEARNING ALGORITHMS;

DYNAMIC MODELS;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; DISCRIMINATION LEARNING; HUMAN; LOCOMOTION; METHODOLOGY; MULTIVARIATE ANALYSIS; NONLINEAR SYSTEM; PROBABILITY; THEORETICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; DISCRIMINATION LEARNING; HUMANS; LOCOMOTION; MARKOV CHAINS; MODELS, THEORETICAL; MULTIVARIATE ANALYSIS; NONLINEAR DYNAMICS; PATTERN RECOGNITION, AUTOMATED;

EID: 69549084570     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2009.37     Document Type: Article

References (31)

1. P. Abbeel, A. Coates, M. Montemerlo, A.Y. Ng, and S. Thrun, "Discriminative Training of Kalman Filters," Proc. Robotics: Science and Systems, 2005.
2. Y. Bar-Shalom and X.-R. Li, Estimation and Tracking: Principles, Techniques, and Software. Artech House, 1993.
3. C.H. Ek, P.H. Torr, and N.D. Lawrence, "Gaussian Process Latent Variable Models for Human Pose Estimation," Proc. Joint Workshop Machine Learning and Multimodal Interaction, 2007.
4. -1 A = I," Linear Algebra and Its Application, vol. 194, pp. 91-108, 1993.
5. -1 A = Q," Linear Algebra and Its Application, vol. 186, pp. 255-275, 1993.
6. Z. Ghahramani and S. Roweis, "Learning Nonlinear Dynamical Systems Using an EM Algorithm," Advances in Neural Information Processing Systems, 1999.
7. R. Greiner and W. Zhou, "Structural Extension to Logistic Regression: Discriminative Parameter Learning of Belief Net Classifiers," Proc. 18th Ann. Nat'l Conf. Artificial Intelligence, 2002.
8. M. Hollander and D.A. Wolfe, Nonparametric Statistical Methods. Wiley, 1973.
9. A.D. Jepson, D.J. Fleet, and T.F. El-Maraghi, "Robust Online Appearance Models for Visual Tracking," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 10, pp. 1296-1311, Oct. 2001.
10. Y. Jing, V. Pav lovic, and J.M. Rehg, "Efficient Discriminative Learning of Bayesian Network Classifier via Boosted Augmented Naive Bayes," Proc. Int'l Conf. Machine Learning, 2005.
11. M. Jordan, "Graphical Models," Statistical Science, special issue on Bayesian statistics, vol. 19, pp. 140-155, 2004.
12. M. Jordan and R. Jacobs, "Hierarchical Mixtures of Experts and the EM Algorithm," Neural Computation, vol. 6, pp. 181-214, 1994.
13. S. Kakade, Y. Teh, and S. Roweis, "An Alternate Objective Function for Markovian Fields," Proc. Int'l Conf. Machine Learning, 2002.
14. M. Kim and V. Pavlovic, "Discriminative Learning of Mixture of Bayesian Network Classifiers for Sequence Classification," Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, 2006.
15. J. Lafferty, A. McCallum, and F. Pereira, "Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data," Proc. Int'l Conf. Machine Learning, 2001.
16. J. Lafferty, X. Zhu, and Y. Liu, "Kernel Conditional Random Fields: Representation and Clique Selection," Proc. Int'l Conf. Machine Learning, 2004.
17. D.J.C. MacKay, "A Practical Bayesian Framework for Back-propagation Networks," Neural Computation, vol. 4, pp. 448-472, 1992.
18. A. Mccallum, D. Freitag, and F. Pereira, "Maximum Entropy Markov Models for Information Extraction and Segmentation," Proc. Int'l Conf. Machine Learning, 2000.
19. A.Y. Ng and M. Jordan, "On Discriminative vs. Generative Classifiers: A Comparison of Logistic Regression and Naive Bayes," Neural Information Processing Systems, 2002.
20. F. Pernkopf and J. Bilmes, "Discriminative versus Generative Parameter and Structure Learning of Bayesian Network Classifiers," Proc. Int'l Conf. Machine Learning, 2005.
21. D. Ross, S. Osindero, and R. Zemel, "Combining Discriminative Features to Infer Complex Trajectories," Proc. Int'l Conf. Machine Learning 2006.
22. F. Sha and F. Pereira, "Shallow Parsing with Conditional Random Fields," Proc. Human Language Technology Conf. - North American Chapter of the Assoc. Computational Linguistics (NAACL), 2003.
23. C. Sminchisescu, A. Kanaujia, Z. Li, and D. Metaxas, "Discriminative Density Propagation for 3D Human Motion Estimation," Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, 2005.
24. M.F. Tappen, C. Liu, E.H. Adelson, and W.T. Freeman, "Learning Gaussian Conditional Random Fields for Low-Level Vision," Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, 2007.
25. L. Taycher, D. Demirdjian, T. Darrell, and G. Shakhnarovich, "Conditional Random People: Tracking Humans with CRFs and Grid Filters," Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, 2006.
26. T.-P. Tian, R. Li, and S. Sclaroff, "Articulated Pose Estimation in a Learned Smooth Space of Feasible Solutions," Proc. IEEE Workshop Computer Vision and Pattern Recognition, 2005.
27. R. Urtasun, D. Fleet, A. Hertzmann, and P. Fua, "Priors for People Tracking from Small Training Sets," Proc. IEEE Int'l Conf. Computer Vision, 2005.
28. R. Urtasun, D.J. Fleet, and P. Fua, "Gaussian Process Dynamical Models for 3d People Tracking," Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, 2006.
29. R. van der Merwe and E. Wan, "The Square-Root Unscented Kalman Filter for State and Parameter-Estimation," Proc. IEEE Int'l Conf. Acoustics, Speech, and Signal Processing, 2001.
30. P. Woodland and D. Povey, "Large Scale Discriminative Training of Hidden Markov Models for Speech Recognition," Computer Speech and Language, vol. 16, no. 1, pp. 25-47, 2002.
31. E.P. Xing, A.Y. Ng, M.I. Jordan, and S. Russell, "Distance Metric Learning, with Application to Clustering with Side Inforination," Advances in Neural Infonnation Processing Systems, 2002.