Kernel Principal Component Analysis
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
A spatio-temporal extension to Isomap nonlinear dimension reduction
ICML '04 Proceedings of the twenty-first international conference on Machine learning
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
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
Cross-View Action Recognition from Temporal Self-similarities
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Gaussian process latent variable models for human pose estimation
MLMI'07 Proceedings of the 4th international conference on Machine learning for multimodal interaction
View and style-independent action manifolds for human activity recognition
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Temporal Extension of Laplacian Eigenmaps for Unsupervised Dimensionality Reduction of Time Series
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Common-sense reasoning for human action recognition
Pattern Recognition Letters
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A novel nonlinear probabilistic feature extraction method, called Spatio-Temporal Gaussian Process Latent Variable Model, is introduced to discover generalised and continuous low dimensional representation of multivariate time series data in the presence of stylistic variations. This is achieved by incorporating a new spatio-temporal constraining prior over latent spaces within the likelihood optimisation of Gaussian Process Latent Variable Models (GPLVM). As a result, the core pattern of multivariate time series is extracted, whereas a style variability is marginalised. We validate the method by qualitative comparison of different GPLVM variants with their proposed spatio-temporal versions. In addition we provide quantitative results on a classification application, i.e. view-invariant action recognition, where imposing spatio-temporal constraints is essential. Performance analysis reveals that our spatio-temporal framework outperforms the state of the art.