Published in

2006 International Workshop on Integrating AI and Data Mining

DOI: 10.1109/aidm.2006.18

Links

Tools

Export citation

Search in Google Scholar

Visualization of Non-vectorial Data Using Twin Kernel Embedding

Proceedings article published in 2006 by Yi Guo, Kwan Ph, Junbin Gao ORCID, Paul W. H. Kwan
This paper is available in a repository.
This paper is available in a repository.

Full text: Download

Green circle
Preprint: archiving allowed
Upload
Green circle
Postprint: archiving allowed
Upload
Red circle
Published version: archiving forbidden
Policy details
Data provided by SHERPA/RoMEO

Abstract

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