129 search results - page 21 / 26 » Clustering-based locally linear embedding |
113
Voted
NIPS
2008
15 years 5 months ago
2008
We continue our study of online prediction of the labelling of a graph. We show a fundamental limitation of Laplacian-based algorithms: if the graph has a large diameter then the ...
125
Voted
IJIT
2004
15 years 4 months ago
2004
In this contribution, the use of a new genetic operator is proposed. The main advantage of using this operator is that it is able to assist the evolution procedure to converge fast...
135
Voted
NIPS
1997
15 years 4 months ago
1997
Nonlinear dimensionality reduction is formulated here as the problem of trying to find a Euclidean feature-space embedding of a set of observations that preserves as closely as p...
152
Voted
ICML
2010
IEEE
15 years 4 months ago
2010
IEEE
Existing approaches to analyzing the asymptotics of graph Laplacians typically assume a well-behaved kernel function with smoothness assumptions. We remove the smoothness assumpti...
134
Voted
FTML
2010
15 years 2 months ago
2010
We give a tutorial overview of several geometric methods for dimension reduction. We divide the methods into projective methods and methods that model the manifold on which the da...