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AUSAI
2006
Springer
13 years 10 months ago
Kernel Laplacian Eigenmaps for Visualization of Non-vectorial Data
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...
Yi Guo, Junbin Gao, Paul Wing Hing Kwan
PAMI
2008
144views more  PAMI 2008»
13 years 6 months ago
Twin Kernel Embedding
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...
Yi Guo, Junbin Gao, Paul W. Kwan
EUROCAST
2003
Springer
138views Hardware» more  EUROCAST 2003»
14 years 4 days ago
Coloring of DT-MRI Fiber Traces Using Laplacian Eigenmaps
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...
Anders Brun, Hae-Jeong Park, Hans Knutsson, Carl-F...
PKDD
2010
Springer
179views Data Mining» more  PKDD 2010»
13 years 5 months ago
Laplacian Spectrum Learning
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...
Pannagadatta K. Shivaswamy, Tony Jebara
TNN
2008
129views more  TNN 2008»
13 years 6 months ago
Data Visualization and Dimensionality Reduction Using Kernel Maps With a Reference Point
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...
Johan A. K. Suykens