Kernel Laplacian Eigenmaps for Visualization of Non-vectorial Data

Author(s)
Guo, Yi
Gao, Junbin
Kwan, Paul Hing
Publication Date
2006
Abstract
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.
Citation
AI 2006: Advances in Artificial Intelligence, p. 1179-1183
ISBN
3540497870
9783540497875
Link
Publisher
Springer
Series
Lecture Notes in Computer Science
Title
Kernel Laplacian Eigenmaps for Visualization of Non-vectorial Data
Type of document
Conference Publication
Entity Type
Publication

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