Supervised Spectral Latent Variable Models
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[1] Miguel Á. Carreira-Perpiñán,et al. The Laplacian Eigenmaps Latent Variable Model , 2007, AISTATS.
[2] Michael I. Jordan,et al. Regression on manifolds using kernel dimension reduction , 2007, ICML '07.
[3] Cristian Sminchisescu,et al. Twin Gaussian Processes for Structured Prediction , 2010, International Journal of Computer Vision.
[4] Neill W Campbell,et al. IEEE International Conference on Computer Vision and Pattern Recognition , 2008 .
[5] Thomas Hofmann,et al. Support vector machine learning for interdependent and structured output spaces , 2004, ICML.
[6] Rajesh P. N. Rao,et al. Learning Shared Latent Structure for Image Synthesis and Robotic Imitation , 2005, NIPS.
[7] Cristian Sminchisescu,et al. Generative modeling for continuous non-linearly embedded visual inference , 2004, ICML.
[8] Liefeng Bo,et al. Structured output-associative regression , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[9] Miguel Á. Carreira-Perpiñán,et al. People Tracking with the Laplacian Eigenmaps Latent Variable Model , 2007, NIPS.
[10] Roland Memisevic,et al. Kernel information embeddings , 2006, ICML.
[11] Mikhail Belkin,et al. Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering , 2001, NIPS.
[12] S T Roweis,et al. Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.
[13] Andrew W. Fitzgibbon,et al. The Joint Manifold Model for Semi-supervised Multi-valued Regression , 2007, 2007 IEEE 11th International Conference on Computer Vision.
[14] Cristian Sminchisescu,et al. Structured output-associative regression , 2009, CVPR.
[15] David J. Fleet,et al. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE Gaussian Process Dynamical Model , 2007 .
[16] Christopher M. Bishop,et al. GTM: The Generative Topographic Mapping , 1998, Neural Computation.
[17] David J. Fleet,et al. Topologically-constrained latent variable models , 2008, ICML '08.
[18] R. Cook. Regression Graphics , 1994 .
[19] Bernhard Schölkopf,et al. Kernel Dependency Estimation , 2002, NIPS.
[20] Michael J. Black,et al. HumanEva: Synchronized Video and Motion Capture Dataset for Evaluation of Articulated Human Motion , 2006 .
[21] Ben Taskar,et al. Max-Margin Markov Networks , 2003, NIPS.
[22] Vikas Sindhwani,et al. On Manifold Regularization , 2005, AISTATS.
[23] Cristian Sminchisescu,et al. Spectral Latent Variable Models for Perceptual Inference , 2007, 2007 IEEE 11th International Conference on Computer Vision.
[24] Cristian Sminchisescu,et al. Fast algorithms for large scale conditional 3D prediction , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[25] George Eastman House,et al. Sparse Bayesian Learning and the Relevance Vector Machine , 2001 .
[26] Cristian Sminchisescu,et al. Generalized Darting Monte Carlo , 2007, AISTATS.
[27] Michael E. Tipping,et al. Probabilistic Principal Component Analysis , 1999 .
[28] David J. Fleet,et al. Priors for people tracking from small training sets , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.
[29] David J. C. MacKay,et al. Comparison of Approximate Methods for Handling Hyperparameters , 1999, Neural Computation.
[30] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[31] J. Tenenbaum,et al. A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.
[32] Neil D. Lawrence,et al. Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models , 2005, J. Mach. Learn. Res..
[33] Simon Haykin,et al. GradientBased Learning Applied to Document Recognition , 2001 .