Name
Abstract | ||
|---|---|---|
High dimensional time series are endemic in applications of machine learning such as robotics (sensor data), computational biology (gene expression data), vision (video sequences) and graphics (motion capture data). Practical nonlinear probabilistic approaches to this data are required. In this paper we introduce the variational Gaussian process dynamical system. Our work builds on recent variational approximations for Gaussian process latent variable models to allow for nonlinear dimensionality reduction simultaneously with learning a dynamical prior in the latent space. The approach also allows for the appropriate dimensionality of the latent space to be automatically determined. We demonstrate the model on a human motion capture data set and a series of high resolution video sequences. |
Year | Venue | Keywords |
|---|---|---|
2011 | neural information processing systems | computational biology,dynamic system,gaussian process,time series,nonlinear dimensionality reduction,machine learning,artificial intelligent,high resolution,latent variable model,pattern recognition |
DocType | Volume | Citations |
Conference | abs/1107.4985 | 31 |
PageRank | References | Authors |
1.88 | 13 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| andreas damianou | 1 | 151 | 17.68 |
| Michalis K. Titsias | 2 | 706 | 42.50 |
| Neil D. Lawrence | 3 | 3411 | 268.51 |