22 search results - page 2 / 5 » Clustering and dimensionality reduction on Riemannian manifo... |
NIPS
2004
13 years 6 months ago
2004
Many machine learning algorithms for clustering or dimensionality reduction take as input a cloud of points in Euclidean space, and construct a graph with the input data points as...
NIPS
2001
13 years 6 months ago
2001
Drawing on the correspondence between the graph Laplacian, the Laplace-Beltrami operator on a manifold, and the connections to the heat equation, we propose a geometrically motiva...
NIPS
2003
13 years 6 months ago
2003
We introduce an information theoretic method for nonparametric, nonlinear dimensionality reduction, based on the infinite cluster limit of rate distortion theory. By constraining...
AMDO
2006
Springer
13 years 8 months ago
2006
Springer
Abstract. We propose motion manifold learning and motion primitive segmentation framework for human motion synthesis from motion-captured data. High dimensional motion capture date...
MM
2004
ACM
13 years 10 months ago
2004
ACM
We consider the problem of learning a mapping function from low-level feature space to high-level semantic space. Under the assumption that the data lie on a submanifold embedded ...