13 search results - page 1 / 3 » Kernel Laplacian Eigenmaps for Visualization of Non-vectoria... |
104
Voted
AUSAI
2006
Springer
15 years 4 months ago
2006
Springer
In this paper, we propose the Kernel Laplacian Eigenmaps for nonlinear dimensionality reduction. This method can be extended to any structured input beyond the usual vectorial data...
93
Voted
PAMI
2008
15 years 21 days ago
2008
Visualization of non-vectorial objects is not easy in practice due to their lack of convenient vectorial representation. Representative approaches are Kernel PCA and Kernel Laplac...
102
Voted
EUROCAST
2003
Springer
15 years 6 months ago
2003
Springer
We propose a novel post processing method for visualization of fiber traces from DT-MRI data. Using a recently proposed non-linear dimensionality reduction technique, Laplacian ei...
124
Voted
PKDD
2010
Springer
14 years 11 months ago
2010
Springer
Abstract. The eigenspectrum of a graph Laplacian encodes smoothness information over the graph. A natural approach to learning involves transforming the spectrum of a graph Laplaci...
119
Voted
TNN
2008
15 years 20 days ago
2008
In this paper, a new kernel-based method for data visualization and dimensionality reduction is proposed. A reference point is considered corresponding to additional constraints ta...