Visualization of Non-vectorial Data Using Twin Kernel Embedding

Y Guo; J Gao; PH Kwan

doi:10.1109/AIDM.2006.18

Visualization of Non-vectorial Data Using Twin Kernel Embedding

Y Guo
, J Gao
, PH Kwan

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

12 Citations (Scopus)

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.

Original language	English
Title of host publication	Proceedings of The 2006 International Workshop on Integrating AI and Data Mining (AIDM'06)
Editors	K-L Ong, K Smith-Miles, V Lee, W-K Ng
Place of Publication	Los Alamitos, United States of America
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	11-17
ISBN (Print)	0769527302
DOIs	https://doi.org/10.1109/AIDM.2006.18
Publication status	Published - 2006
Event	AIDM 2006: International Workshop on Integrating AI and Data Mining - Hobart, Australia Duration: 4 Dec 2006 → 8 Dec 2006

Conference

Conference	AIDM 2006: International Workshop on Integrating AI and Data Mining
City	Hobart, Australia
Period	4/12/06 → 8/12/06

Keywords

Pattern Recognition and Data Mining

Access to Document

10.1109/AIDM.2006.18

https://hdl.handle.net/1959.11/1021

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Cite this

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title = "Visualization of Non-vectorial Data Using Twin Kernel Embedding",

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.",

keywords = "Pattern Recognition and Data Mining",

author = "Y Guo and J Gao and PH Kwan",

year = "2006",

doi = "10.1109/AIDM.2006.18",

language = "English",

isbn = "0769527302",

pages = "11--17",

editor = "K-L Ong and K Smith-Miles and V Lee and W-K Ng",

booktitle = "Proceedings of The 2006 International Workshop on Integrating AI and Data Mining (AIDM'06)",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

note = "AIDM 2006: International Workshop on Integrating AI and Data Mining ; Conference date: 04-12-2006 Through 08-12-2006",

}

Guo, Y, Gao, J & Kwan, PH 2006, Visualization of Non-vectorial Data Using Twin Kernel Embedding. in K-L Ong, K Smith-Miles, V Lee & W-K Ng (eds), Proceedings of The 2006 International Workshop on Integrating AI and Data Mining (AIDM'06). Institute of Electrical and Electronics Engineers (IEEE), Los Alamitos, United States of America, pp. 11-17, AIDM 2006: International Workshop on Integrating AI and Data Mining, Hobart, Australia, 4/12/06. https://doi.org/10.1109/AIDM.2006.18

Visualization of Non-vectorial Data Using Twin Kernel Embedding. / Guo, Y; Gao, J; Kwan, PH.
Proceedings of The 2006 International Workshop on Integrating AI and Data Mining (AIDM'06). ed. / K-L Ong; K Smith-Miles; V Lee; W-K Ng. Los Alamitos, United States of America: Institute of Electrical and Electronics Engineers (IEEE), 2006. p. 11-17.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Visualization of Non-vectorial Data Using Twin Kernel Embedding

AU - Guo, Y

AU - Gao, J

AU - Kwan, PH

PY - 2006

Y1 - 2006

N2 - 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.

AB - 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.

KW - Pattern Recognition and Data Mining

U2 - 10.1109/AIDM.2006.18

DO - 10.1109/AIDM.2006.18

M3 - Conference contribution

SN - 0769527302

SP - 11

EP - 17

BT - Proceedings of The 2006 International Workshop on Integrating AI and Data Mining (AIDM'06)

A2 - Ong, K-L

A2 - Smith-Miles, K

A2 - Lee, V

A2 - Ng, W-K

PB - Institute of Electrical and Electronics Engineers (IEEE)

CY - Los Alamitos, United States of America

T2 - AIDM 2006: International Workshop on Integrating AI and Data Mining

Y2 - 4 December 2006 through 8 December 2006

ER -

Visualization of Non-vectorial Data Using Twin Kernel Embedding

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