253 search results - page 6 / 51 » Principal Graphs and Manifolds |
ECCV
2002
Springer
16 years 2 months ago
2002
Springer
Edge detection depends not only upon the assumed model of what an edge is, but also on how this model is represented. The problem of how to represent the edge model is typically ne...
91
Voted
ECCV
2000
Springer
16 years 2 months ago
2000
Springer
In recent years several techniques have been proposed for modelling the low-dimensional manifolds, or `subspaces', of natural images. Examples include principal component anal...
ESANN
2007
15 years 2 months ago
2007
Mixtures of probabilistic principal component analyzers model high-dimensional nonlinear data by combining local linear models. Each mixture component is specifically designed to...
90
Voted
PR
2007
15 years 5 days ago
2007
Isomap is one of widely-used low-dimensional embedding methods, where geodesic distances on a weighted graph are incorporated with the classical scaling (metric multidimensional s...
107
Voted
NIPS
2004
15 years 2 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...