Archambeau, Cédric
[UCL]
Verleysen, Michel
[UCL]
In many practical applications, the data is organized along a manifold of lower dimension than the dimension of the embedding space. This additional information can be used when learning the model parameters of Gaussian mixtures. Based on a mismatch measure between the Euclidian and the geodesic distance, manifold constrained responsibilities are introduced. Experiments in density estimation show that manifold Gaussian mixtures outperform ordinary Gaussian mixtures.
Bibliographic reference |
Archambeau, Cédric ; Verleysen, Michel. Manifold constrained finite Gaussian mixtures.8th International Work-Conference on Artificial Neural Networks (IWANN 2005) (Barcelona (Spain), du 08/06/2005 au 10/06/2005). In: Lecture Notes in Computer Science, Vol. 3512, p. 820-828 (2005) |
Permanent URL |
http://hdl.handle.net/2078.1/60935 |