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31

AUSAI
2006
Springer

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Kernel Laplacian Eigenmaps for Visualization of Non-vectorial Data

14 years 2 months ago

Kernel Laplacian Eigenmaps for Visualization of Non-vectorial Data

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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, enabling the visualization of a wider range of data in low dimension once suitable kernels are defined. Comparison with related methods based on MNIST handwritten digits data set supported the claim of our approach. In addition to nonlinear dimensionality reduction, this approach makes visualization and related applications on non-vectorial data possible.

Yi Guo, Junbin Gao, Paul Wing Hing Kwan

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Added	20 Aug 2010
Updated	20 Aug 2010
Type	Conference
Year	2006
Where	AUSAI
Authors	Yi Guo, Junbin Gao, Paul Wing Hing Kwan

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