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...
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...
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...
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...
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...