Name
Abstract | ||
|---|---|---|
We investigate the geometrical structure of probabilistic generative dimensionality reduction models using the tools of Riemannian geometry. We explicitly define a distribution over the natural metric given by the models. We provide the necessary algorithms to compute expected metric tensors where the distribution over mappings is given by a Gaussian process. We treat the corresponding latent variable model as a Riemannian manifold and we use the expectation of the metric under the Gaussian process prior to define interpolating paths and measure distance between latent points. We show how distances that respect the expected metric lead to more appropriate generation of new data. |
Year | Venue | Field |
|---|---|---|
2014 | UNCERTAINTY IN ARTIFICIAL INTELLIGENCE | Applied mathematics,Information geometry,Topology,Mathematical optimization,Fisher information metric,Computer science,Riemannian manifold,Intrinsic metric,Metric (mathematics),Gaussian process,Statistical manifold,Riemannian geometry |
DocType | Volume | Citations |
Journal | abs/1411.7432 | 3 |
PageRank | References | Authors |
0.45 | 13 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alessandra Tosi | 1 | 3 | 0.45 |
| Søren Hauberg | 2 | 3 | 0.45 |
| Alfredo Vellido | 3 | 3 | 0.45 |
| Neil D. Lawrence | 4 | 3411 | 268.51 |