DocumentCode :
2902925
Title :
Visualization of Non-vectorial Data Using Twin Kernel Embedding
Author :
Guo, Yi ; Gao, Junbin ; Kwan, Paul W H
Author_Institution :
Sch. of Math, Stat. & Comput. Sci., New England Univ., Armidale, NSW
fYear :
2006
fDate :
Dec. 2006
Firstpage :
11
Lastpage :
17
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
Keywords :
data visualisation; MNIST handwritten digit database; data visualization; nonvectorial data; twin kernel embedding; Australia; Computer science; Covariance matrix; Data visualization; Databases; Image reconstruction; Information technology; Kernel; Laplace equations; Principal component analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Integrating AI and Data Mining, 2006. AIDM '06. International Workshop on
Conference_Location :
Hobart, Tas.
Print_ISBN :
0-7695-2730-2
Type :
conf
DOI :
10.1109/AIDM.2006.18
Filename :
4030707
Link To Document :
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