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|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||Statistics to Oct 2018:||Visitors: 103
|Appears in Collections:||Conference Publication|
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