181 search results - page 2 / 37 » Graph Embedding: A General Framework for Dimensionality Redu... |
NIPS
2003
13 years 6 months ago
2003
Spectral methods for nonlinear dimensionality reduction (NLDR) impose a neighborhood graph on point data and compute eigenfunctions of a quadratic form generated from the graph. W...
SDM
2007
SIAM
13 years 6 months ago
2007
SIAM
Nonlinear dimensionality reduction methods often rely on the nearest-neighbors graph to extract low-dimensional embeddings that reliably capture the underlying structure of high-d...
ECCV
2004
Springer
14 years 6 months ago
2004
Springer
In recent work, we presented a framework for many-to-many matching of multi-scale feature hierarchies, in which features and their relations were captured in a vertex-labeled, edge...
IJON
2010
13 years 1 months ago
2010
Graph construction plays a key role on learning algorithms based on graph Laplacian. However, the traditional graph construction approaches of -neighborhood and k-nearest-neighbor...
PRL
2010
13 years 3 months ago
2010
In this paper, our main contribution is a framework for the automatic configuration of any spectral dimensionality reduction methods. This is achieved, first, by introducing the m...