Manifold Constrained Finite Gaussian Mixtures

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.

• Type: Conference
• Year: 2005
• Where: IWANN
• Authors: Cédric Archambeau, Michel Verleysen
• Keywords: Gaussian Mixtures, IWANN 2005, Manifold, Ordinary Gaussian Mixtures