Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A Hierarchical Latent Variable Model for Data Visualization
IEEE Transactions on Pattern Analysis and Machine Intelligence
GTM: the generative topographic mapping
Neural Computation
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Local distance preservation in the GP-LVM through back constraints
ICML '06 Proceedings of the 23rd international conference on Machine learning
3D People Tracking with Gaussian Process Dynamical Models
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models
The Journal of Machine Learning Research
Image modeling using tree structured conditional random fields
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
WiFi-SLAM using Gaussian process latent variable models
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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Continuous Hand Gesture Recognition in the Learned Hierarchical Latent Variable Space
AMDO '08 Proceedings of the 5th international conference on Articulated Motion and Deformable Objects
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Journal on Image and Video Processing - Anthropocentric Video Analysis: Tools and Applications
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Using Hierarchical Models for 3D Human Body-Part Tracking
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
Discriminative human action recognition in the learned hierarchical manifold space
Image and Vision Computing
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Modeling style and variation in human motion
Proceedings of the 2010 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Learning GP-BayesFilters via Gaussian process latent variable models
Autonomous Robots
Behavioural analysis with movement cluster model for concurrent actions
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Interactive region-based linear 3D face models
ACM SIGGRAPH 2011 papers
Two Distributed-State Models For Generating High-Dimensional Time Series
The Journal of Machine Learning Research
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
Cohort-based kernel visualisation with scatter matrices
Pattern Recognition
Gaussian process motion graph models for smooth transitions among multiple actions
Computer Vision and Image Understanding
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Simultaneous particle tracking in multi-action motion models with synthesized paths
Image and Vision Computing
Two-layer dual gait generative models for human motion estimation from a single camera
Image and Vision Computing
Proceedings of the ACM Symposium on Applied Perception
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The Gaussian process latent variable model (GP-LVM) is a powerful approach for probabilistic modelling of high dimensional data through dimensional reduction. In this paper we extend the GP-LVM through hierarchies. A hierarchical model (such as a tree) allows us to express conditional independencies in the data as well as the manifold structure. We first introduce Gaussian process hierarchies through a simple dynamical model, we then extend the approach to a more complex hierarchy which is applied to the visualisation of human motion data sets.