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13

PAMI
2008

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Twin Kernel Embedding

13 years 4 months ago

Twin Kernel Embedding

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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 Laplacian Eigenmaps introduced recently in our research. Extending our earlier work, we propose in this paper a new algorithm called Twin Kernel Embedding (TKE) that preserves the similarity structure of input data in the latent space. Experimental evaluation on MNIST handwritten digit database verifies that TKE outperforms related methods.

Yi Guo, Junbin Gao, Paul W. Kwan

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Convenient Vectorial Representation | Kernel Laplacian Eigenmaps | Kernel PCA | PAMI 2008 |

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Added	14 Dec 2010
Updated	14 Dec 2010
Type	Journal
Year	2008
Where	PAMI
Authors	Yi Guo, Junbin Gao, Paul W. Kwan

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