GTM: the generative topographic mapping
Neural Computation
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Mixtures of probabilistic principal component analyzers
Neural Computation
Learning a kernel matrix for nonlinear dimensionality reduction
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Style-based inverse kinematics
ACM SIGGRAPH 2004 Papers
Priors for People Tracking from Small Training Sets
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Large margin non-linear embedding
ICML '05 Proceedings of the 22nd international conference on Machine learning
Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models
The Journal of Machine Learning Research
Gaussian Process Dynamical Models for Human Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical Gaussian process latent variable models
Proceedings of the 24th international conference on Machine learning
Discriminative Gaussian process latent variable model for classification
Proceedings of the 24th international conference on Machine learning
Topologically-constrained latent variable models
Proceedings of the 25th international conference on Machine learning
Kernels, regularization and differential equations
Pattern Recognition
Feature extraction using constrained maximum variance mapping
Pattern Recognition
Ambiguity Modeling in Latent Spaces
MLMI '08 Proceedings of the 5th international workshop on Machine Learning for Multimodal Interaction
Speech-Driven Facial Animation Using a Shared Gaussian Process Latent Variable Model
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Modeling human locomotion with topologically constrained latent variable models
Proceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation
Gaussian process latent variable models for human pose estimation
MLMI'07 Proceedings of the 4th international conference on Machine learning for multimodal interaction
Tracking human pose with multiple activity models
Pattern Recognition
Relevance units latent variable model and nonlinear dimensionality reduction
IEEE Transactions on Neural Networks
Dual gait generative models for human motion estimation from a single camera
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
Learning GP-BayesFilters via Gaussian process latent variable models
Autonomous Robots
Computer Vision and Image Understanding
Probabilistic feature extraction from multivariate time series using spatio-temporal constraints
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Facial movement based recognition
MIRAGE'11 Proceedings of the 5th international conference on Computer vision/computer graphics collaboration techniques
Gaussian process motion graph models for smooth transitions among multiple actions
Computer Vision and Image Understanding
Continuous character control with low-dimensional embeddings
ACM Transactions on Graphics (TOG) - SIGGRAPH 2012 Conference Proceedings
Simultaneous particle tracking in multi-action motion models with synthesized paths
Image and Vision Computing
Proceedings of the ACM Symposium on Applied Perception
Large-margin multi-view Gaussian process for image classification
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
Nonparametric guidance of autoencoder representations using label information
The Journal of Machine Learning Research
OBST-based segmentation approach to financial time series
Engineering Applications of Artificial Intelligence
Efficient tracking using a robust motion estimation technique
Multimedia Tools and Applications
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The Gaussian process latent variable model (GP-LVM) is a generative approach to nonlinear low dimensional embedding, that provides a smooth probabilistic mapping from latent to data space. It is also a non-linear generalization of probabilistic PCA (PPCA) (Tipping & Bishop, 1999). While most approaches to non-linear dimensionality methods focus on preserving local distances in data space, the GP-LVM focusses on exactly the opposite. Being a smooth mapping from latent to data space, it focusses on keeping things apart in latent space that are far apart in data space. In this paper we first provide an overview of dimensionality reduction techniques, placing the emphasis on the kind of distance relation preserved. We then show how the GP-LVM can be generalized, through back constraints, to additionally preserve local distances. We give illustrative experiments on common data sets.