Please use this identifier to cite or link to this item: https://une.intersearch.com.au/unejspui/handle/1959.11/1021
Title: Visualization of Non-vectorial Data Using Twin Kernel Embedding
Contributor(s): Guo, Y (author); Gao, J (author); Kwan, PH  (author)
Publication Date: 2006
DOI: 10.1109/AIDM.2006.18
Handle Link: https://hdl.handle.net/1959.11/1021
Abstract: Visualization of non-vectorial objects is not easy in practicedue to their lack of convenient vectorial representation.Representative approaches are Kernel PCA and KernelLaplacian Eigenmaps introduced recently in our research.Extending our earlier work, we propose in this papera new algorithm called Twin Kernel Embedding (TKE)that preserves the similarity structure of input data in the latentspace. Experimental evaluation on MNIST handwrittendigit database verifies that TKE outperforms related methods.
Publication Type: Conference Publication
Conference Name: International Workshop on Integrating AI and Data Mining (AIDM'06), Hobart, Tasmania, 04-08/12/2006
Conference Details: International Workshop on Integrating AI and Data Mining (AIDM'06), Hobart, Tasmania, 04-08/12/2006
Source of Publication: Proceedings of The 2006 International Workshop on Integrating AI and Data Mining (AIDM'06), p. 11-17
Publisher: IEEE Computer Society
Place of Publication: Los Alamitos, CA, USA
Field of Research (FOR): 080109 Pattern Recognition and Data Mining
Peer Reviewed: Yes
HERDC Category Description: E1 Refereed Scholarly Conference Publication
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