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Ali Rahimi, Ben Recht, Trevor Darrell, "Learning to Transform Time Series with a Few Examples," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 10, pp. 17591775, October, 2007.  
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@article{ 10.1109/TPAMI.2007.1001, author = {Ali Rahimi and Ben Recht and Trevor Darrell}, title = {Learning to Transform Time Series with a Few Examples}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {29}, number = {10}, issn = {01628828}, year = {2007}, pages = {17591775}, doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2007.1001}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Pattern Analysis and Machine Intelligence TI  Learning to Transform Time Series with a Few Examples IS  10 SN  01628828 SP1759 EP1775 EPD  17591775 A1  Ali Rahimi, A1  Ben Recht, A1  Trevor Darrell, PY  2007 KW  Semisupervised learning KW  examplebased tracking KW  manifold learning KW  nonlinear system identification VL  29 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
[1] J.B. Tenenbaum, V. de Silva, and J.C. Langford, “A Global Geometric Framework for Nonlinear Dimensionality Reduction,” Science, vol. 290, no. 5500, pp. 23192323, 2000.
[2] S. Roweis and L. Saul, “Nonlinear Dimensionality Reduction by Locally Linear Embedding,” Science, vol. 290, no. 5500, pp. 23232326, 2000.
[3] M. Belkin and P. Niyogi, “Laplacian Eigenmaps for Dimensionality Reduction and Data Representation,” Neural Computation, vol. 15, no. 6, pp. 13731396, 2003.
[4] D. Donoho and C. Grimes, “Hessian Eigenmaps: New Locally Linear Embedding Techniques for Highdimensional Data,” Technical Report TR200308, Dept. of Statistics, Stanford Univ., 2003.
[5] K. Weinberger and L. Saul, “Unsupervised Learning of Image Manifolds by Semidefinite Programming,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2004.
[6] M. Brand, “Charting a Manifold,” Proc. Conf. Neural Information Processing Systems, 2002.
[7] M. Balasubramanian, E.L. Schwartz, J.B. Tenenbaum, V. de Silva, and J.C. Langford, “The Isomap Algorithm and Topological Stability,” Science, vol. 295, no. 5552, 2002.
[8] O. Jenkins and M. Mataric, “A SpatioTemporal Extension to Isomap Nonlinear Dimension Reduction,” Proc. Int'l Conf. Machine Learning, 2004.
[9] J. Ham, D. Lee, and L. Saul, “Learning High Dimensional Correspondences from Low Dimensional Manifolds,” Proc. Int'l Conf. Machine Learning, 2003.
[10] R. Pless and I. Simon, “Using Thousands of Images of an Object,” Proc. Conf. Computer Vision, Pattern Recognition, and Image Processing, 2002.
[11] X. Zhu, Z. Ghahramani, and J. Lafferty, “SemiSupervised Learning Using Gaussian Fields and Harmonic Functions,” Proc. Int'l Conf. Machine Learning, 2003.
[12] M. Belkin, I. Matveeva, and P. Niyogi, “Regularization and SemiSupervised Learning on Large Graphs,” Proc. 17th Ann. Conf. Computational Learning Theory, 2004.
[13] J. Lafferty, A. McCallum, and F. Pereira, “Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data,” Proc. Int'l Conf. Machine Learning, pp. 282289, 2001.
[14] A. Smola, S. Mika, B. Schoelkopf, and R.C. Williamson, “Regularized Principal Manifolds,” J. Machine Learning, vol. 1, pp. 179209, 2001.
[15] A. Rahimi, B. Recht, and T. Darrell, “Learning Appearance Manifolds from Video,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2005.
[16] Z. Ghahramani and S. Roweis, “Learning Nonlinear Dynamical Systems Using an EM Algorithm,” Neural Information Processing Systems (NIPS), pp. 431437, 1998.
[17] H. Valpola and J. Karhunen, “An Unsupervised Ensemble Learning Method for Nonlinear Dynamic StateSpace Models,” Neural Computation, vol. 14, no. 11, pp. 26472692, 2002.
[18] L. Ljung, System Identification: Theory for the User. PrenticeHall, 1987.
[19] A. Juditsky, H. Hjalmarsson, A. Benveniste, B. Delyon, L. Ljung, J. Sjöberg, and Q. Zhang, “Nonlinear BlackBox Models in System Identification: Mathematical Foundations,” Automatica, vol. 31, no. 12, pp. 17251750, 1995.
[20] K.C. Lee and D. Kriegman, “Online Learning of Probabilistic Appearance Manifolds for VideoBased Recognition and Tracking,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2005.
[21] G. Doretto, A. Chiuso, and Y.W.S. Soatto, “Dynamic Textures,” Int'l J. Computer Vision, vol. 51, no. 2, pp. 91109, 2003.
[22] O. Bousquet and A. Elisseeff, “Stability and Generalization,” Journal of Machine Learning Research, 2002.
[23] M.P.T. Evgeniou and T. Poggio, “Regularization Networks and Support Vector Machines,” Advances in Computational Math., 2000.
[24] G. Wahba, “Spline Models for Observational Data,” Proc. SIAM, vol. 59, 1990.
[25] B. Schölkopf, R. Herbrich, A. Smola, and R. Williamson, “A Generalized Representer Theorem,” Proc. Neural Networks and Computational Learning Theory, NeuroCOLT Technical Report NCTR00081, 2000.
[26] V. Vapnik, Statistical Learning Theory. Wiley, 1998.
[27] T. De Bie and N. Cristianini, “Convex Methods for Transduction,” Proc. Conf. Neural Information Processing Systems, 2003.
[28] T. Joachims, “Transductive Inference for Text Classification Using Support Vector Machines,” Proc. Int'l Conf. Machine Learning, pp.200209, 1999.
[29] K. Bennett and A. Demiriz, “SemiSupervised Support Vector Machines,” Proc. Conf. Advances in Neural Information Processing Systems, pp. 368374, 1998.
[30] R. Rifkin, G. Yeo, and T. Poggio, “Regularized Least Squares Classification,” Proc. Conf. Advances in Learning Theory: Methods, Model and Applications, NATO Science Series III: Computer and Systems Sciences, vol. 190, 2003.
[31] T. Kailath, A.H. Sayed, and B. Hassibi, Linear Estimation. Prentice Hall, 2000.
[32] A. Rahimi, “Learning to Transform Time Series with a Few Examples,” PhD dissertation, Massachusetts Inst. of Technology, Computer Science and AI Lab, Cambridge, 2005.
[33] D. Fokkema, G. Sleijpen, and H. van der Vorst, “JacobiDavidson Style Qr and Qz Algorithms for the Reduction of Matrix Pencils,” SIAM J. Scientific Computing, vol. 20, no. 1, pp. 94125, 1998.
[34] J. Patten, B. Recht, and H. Ishii, “Audiopad: A TagBased Interface for Musical Performance,” Proc. Conf. New Interfaces for Musical Expression, 2002.
[35] J. Patten, H. Ishii, J. Hines, and G. Pangaro, “Sensetable: A Wireless Object Tracking Platform for Tangible User Interfaces,” Proc. Conf. Human Factors in Computing Systems, 2001.
[36] H. Sidenbladh, M.J. Black, and D. Fleet, “Stochastic Tracking of 3D Human Figures Using 2D Image Motion,” Proc. European Conf. Computer Vision, pp. 702718, 2000.
[37] C. Bregler and J. Malik, “Tracking People with Twists and Exponential Maps,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 1998.
[38] A. Rahimi, “Spline Drawing Tool for MATLAB,” MIT CSAIL http://people.csail.mit.edu/rahimi/splines html, technical report, Aug. 2005.
[39] A.A. Efros, A.C. Berg, G. Mori, and J. Malik, “Recognizing Action at a Distance,” Proc. Int'l Conf. Computer Vision, 2003.
[40] G. Shakhnarovich, P. Viola, and T. Darrell, “Fast Pose Estimation with Parameter Sensitive Hashing,” Proc. Int'l Conf. Computer Vision, 2003.
[41] A.M. Elgammal, “Learning to Track: Conceptual Manifold Map for ClosedForm Tracking,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 724730, 2005.
[42] A. Agarwal and B. Triggs, “3D Human Pose from Silhouettes by Relevance Vector Regression,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2004.
[43] A. Agarwal, A. Hertzmann, and S.M.S.D.H. Salesin, “KeyframeBased Tracking for Rotoscoping and Animation,” Proc. ACM SIGGRAPH, 2004.
[44] A. Agarwala, “Snaketoonz: A SemiAutomatic Approach to Creating Cel Animation from Video,” Proc. Int'l Symp. NonPhotorealistic Animation and Rendering, http://www.agarwala.org/Pagessnaketoonz.html , 2002.
[45] K. Grauman and T. Darrell, “Fast Contour Matching Using Approximate Earth Mover's Distance,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2004.
[46] A. Rahimi and B. Recht, “Estimating Observation Functions in Dynamical Systems Using Unsupervised Regression,” Proc. Advances in Neural Information Processing Systems, 2006.
[47] K. Grauman and T. Darrell, “The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features,” Proc. Int'l Conf. Computer Vision, 2005.
[48] N. Roy and A. McCallum, “Toward Optimal Active Learning through Sampling Estimation of Error Reduction,” Proc. IEEE Int'l Conf. Machine Learning, pp. 441448, 2001.
[49] G. Schohn and D. Cohn, “Less Is More: Active Learning with Support Vector Machines,” Proc. IEEE Int'l Conf. Machine Learning, pp. 839846, 2000.
[50] R. Yan, J. Yang, and A. Hauptmann, “Automatically Labeling Video Data Using MultiClass Active Learning,” Proc. Int'l Conf. Computer Vision, pp. 516523, 2003.
[51] D.P. Bertsekas, A. Nedic, and A.E. Ozdaglar, Convex Analysis and Optimization. Athena Scientific, 2001.