
This Article  
 
Share  
Bibliographic References  
Add to:  
Digg Furl Spurl Blink Simpy Del.icio.us Y!MyWeb  
Search  
 
ASCII Text  x  
L. Sigal, R. Memisevic, D. J. Fleet, "Shared Kernel Information Embedding for Discriminative Inference," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 4, pp. 778790, April, 2012.  
BibTex  x  
@article{ 10.1109/TPAMI.2011.154, author = {L. Sigal and R. Memisevic and D. J. Fleet}, title = {Shared Kernel Information Embedding for Discriminative Inference}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {34}, number = {4}, issn = {01628828}, year = {2012}, pages = {778790}, doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2011.154}, 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  Shared Kernel Information Embedding for Discriminative Inference IS  4 SN  01628828 SP778 EP790 EPD  778790 A1  L. Sigal, A1  R. Memisevic, A1  D. J. Fleet, PY  2012 KW  pose estimation KW  operating system kernels KW  human pose inference KW  shared kernel information embedding KW  discriminative inference KW  latent variable models KW  latent space KW  coherent joint density KW  Kernel KW  Manifolds KW  Training KW  Bandwidth KW  Data models KW  Estimation KW  Probabilistic logic KW  mutual information. KW  Latent variable models KW  kernel information embedding KW  inference KW  nonparametric VL  34 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
[1] B. Schölkopf, A. Smola, and K.R. Müller, "Nonlinear Component Analysis as a Kernel Eigenvalue Problem," Neural Computation, vol. 10, pp. 12991319, July 1998.
[2] J.B. Tenenbaum, V. Silva, and J.C. Langford, "A Global Geometric Framework for Nonlinear Dimensionality Reduction," Science, vol. 290, no. 5500, pp. 23192323, Dec. 2000.
[3] S.T. Roweis and L.K. Saul, "Nonlinear Dimensionality Reduction by Locally Linear Embedding," Science, vol. 290, pp. 23232326, 2000.
[4] Y. Bengio., J.F. Paiement, P. Vincent, O. Delalleau, N.L. Roux, and M. Ouimet, "OutofSample Extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering," Proc. Advances in Neural Information Processing Systems, pp. 177184, 2004.
[5] M.E. Tipping and C.M. Bishop, "Probabilistic Principal Component Analysis," J. Royal Statistical Society, Series B, vol. 61, pp. 611622, 1999.
[6] N.D. Lawrence, "Probabilistic NonLinear Principal Component Analysis with Gaussian Process Latent Variable Models," J. Machine Learning Research, vol. 6, pp. 17831816, Nov. 2005.
[7] P. Meinicke, S. Klanke, R. Memisevic, and H. Ritter, "Principal Surfaces from Unsupervised Kernel Regression," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 9, pp. 13791391, Sept. 2005.
[8] R. Memisevic, "Kernel Information Embeddings," Proc. Int'l Conf. Machine Learing, pp. 633640, 2006.
[9] L. Sigal, R. Memisevic, and D. Fleet, "Shared Kernel Information Embedding for Discriminative Inference," Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, pp. 28522859, 2009.
[10] A. Agarwal and B. Triggs, "Learning to Track 3D Human Motion from Silhouettes," Proc. Int'l Conf. Machine Learning, pp. 916, 2004.
[11] A. Agarwal and B. Triggs, "3D Human Pose from Silhouettes by Relevance Vector Regression," Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 882888, 2004.
[12] C. Ek, P. Torr, and N. Lawrence, "Gaussian Process Latent Variable Models for Human Pose Estimation," Proc. Int'l Conf. Machine Learning for Multimodal Interaction, pp. 132143, 2007.
[13] T. Jaeggli, E. KollerMeier, and L.V. Gool, "Monocular Tracking with a Mixture of ViewDependent Learned Models," Proc. Conf. Articulated Motion and Deformable Objects, pp. 494503, 2006.
[14] A. Kanaujia, C. Sminchisescu, and D. Metaxas, "SemiSupervised Hierarchical Models for 3D Human Pose Reconstruction," Proc. IEEE Conf. Computer Vision Pattern Recognition, 2007.
[15] R. Navaratnam, A. Fitzgibbon, and R. Cipolla, "The Joint Manifold Model for SemiSupervised MultiValued Regression," Proc. 11th IEEE Int'l Conf. Computer Vision, 2007.
[16] G. Shakhnarovich, P. Viola, and T. Darrell, "Fast Pose Estimation with ParameterSensitive Hashing," Proc. Ninth IEEE Int'l Conf. Computer Vision, vol. 2, pp. 750759, 2003.
[17] 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, vol. 1, pp. 390397, 2005.
[18] Z. Lu, M. CarreiraPerpinan, and C. Sminchisescu, "People Tracking with the Laplacian Eigenmaps Latent Variable Model," Advances in Neural Information Processing Systems 20, J. Platt, D. Koller, Y. Singer, and S. Roweis, eds. MIT Press, pp. 17051712, 2008.
[19] R. Urtasun and T. Darrell, "Local Probabilistic Regression for ActivityIndependent Human Pose Inference," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2008.
[20] L. Sigal, A. Balan, and M.J. Black, "Combined Discriminative and Generative Articulated Pose and NonRigid Shape Estimation," Proc. Neural Information Processing Systems, 2007.
[21] T. de Campos and D. Murray, "RegressionBased Hand Pose Estimation from Multiple Cameras," Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 782789, 2006.
[22] M.Á. CarreiraPerpiñán, "Reconstruction of Sequential Data with Probabilistic Models and Continuity Constraints," Proc. Neural Information Processing Systems, pp. 414420, 1999.
[23] A. Thayananthan, R. Navaratnam, B. Stenger, P. Torr, and R. Cipolla, "Multivariate Relevance Vector Machines for Tracking," Proc. European Conf. Computer Vision, pp. 124138, 2006.
[24] L. Bo and C. Sminchisescu, "Twin Gaussian Processes for Structured Prediction," Int'l J. Computer Vision, 2010.
[25] A. Shon, K. Grochow, A. Hertzmann, and R. Rao, "Learning Latent Structure for Image Synthesis and Robotic Imitation," Proc. Neural Information Processing Systems, pp. 12331240, 2006.
[26] A. Kanaujia, C. Sminchisescu, and D. Metaxas, "Spectral Latent Variable Models for Perceptual Inference," Proc. IEEE Int'l Conf. Computer Vision, 2007.
[27] J. QuiñoneroCandela and C. Rasmussen, "A Unifying View of Sparse Approximate Gaussian Process Regression," J. Machine Learning Research, vol. 6, pp. 19391959, 2006.
[28] M.A. CarreiraPerpiñan and Z. Lu, "The Laplacian Eigenmaps Latent Variable Model," J. Machine Learning Research W&P, vol. 2, pp. 5966, 2007.
[29] R. Memisevic, "NonLinear Latent Factor Models for Revealing Structure in HighDimensional Data," PhD dissertation, Univ. of Toronto, 2008.
[30] T.M. Cover and J.A. Thomas, Elements of Information Theory. John Wiley & Sons, 1991.
[31] D.W. Scott, Multivariate Density Estimation: Theory, Practice, and Visualization (Wiley series in probability and statistics). Wiley, Sept. 1992.
[32] T. Brox, B. Rosenhahn, D. Cremers, and H.P. Seidel, "Nonparametric Density Estimation with Adaptive Anisotropic Kernels for Human Motion Tracking," Proc. Second Int'l Workshop Human Motion, 2007.
[33] M. Kuss and T. Graepel, "The Geometry of Kernel Canonical Correlation Analysis," Technical Report 108, Max Planck Inst. for Biological Cybernetics, Tübingen, Germany, May 2003.
[34] M. Salzmann, C.H. Ek, R. Urtasun, and T. Darrell, "Factorized Orthogonal Latent Spaces," Proc. 13th Int'l Conf. Artificial Intelligence and Statistics, 2010.
[35] D. Comaniciu and P. Meer, "Mean Shift: A Robust Approach Toward Feature Space Analysis," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 603619, May 2002.
[36] D.J.C. MacKay, Information Theory, Inference, and Learning Algorithms. Cambridge Univ. Press, 2003.
[37] V. Raykar and R. Duraiswami, The Improved Fast Gauss Transform with Applications to Machine Learning. MIT Press, 2006.
[38] B. Han, D. Comaniciu, Y. Zhu, and L. Davis, "Sequential Kernel Density Approximation: Application to RealTime Visual Tracking," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 30, no. 7, pp. 11861197, July 2008.
[39] L. Sigal and M.J. Black, "HumanEva: Synchronized Video and Motion Capture Data Set for Evaluation of Articulated Human Motion," Technical Report CS0608, Brown Univ., 2006.
[40] L. Bo, C. Sminchisescu, A. Kanaujia, and D. Metaxas, "Fast Algorithms for Large Scale Conditional 3D Prediction," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2008.
[41] M. Ranzato, C. Poultney, S. Chopra, and Y. Lecun, "Efficient Learning of Sparse Representations with an EnergyBased Model," Proc. Advances in Neural Information Processing Systems, 2006.
[42] M.Á. CarreiraPerpiñán and Z. Lu, "Parametric Dimensionality Reduction by Unsupervised Regression," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 18951902, 2010.