Kernel Laplacian Eigenmaps for Visualization of Non-vectorial Data

Yi Guo; Junbin Gao; Paul Hing Kwan

doi:10.1007/11941439_144

Kernel Laplacian Eigenmaps for Visualization of Non-vectorial Data

Yi Guo, Junbin Gao, Paul Hing Kwan

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

Abstract

In this paper, we propose the Kernel Laplacian Eigenmaps for nonlinear dimensionality reduction. This method can be extended to any structured input beyond the usual vectorial data, enabling the visualization of a wider range of data in low dimension once suitable kernels are defined. Comparison with related methods based on MNIST handwritten digits data set supported the claim of our approach. In addition to nonlinear dimensionality reduction, this approach makes visualization and related applications on non-vectorial data possible.

Original language	English
Title of host publication	AI 2006: Advances in Artificial Intelligence
Editors	A Sattar, BH Kang
Place of Publication	Berlin, Germany
Publisher	Springer
Pages	1179-1183
ISBN (Print)	3540497870, 9783540497875
DOIs	https://doi.org/10.1007/11941439_144
Publication status	Published - 2006
Event	AI 2006: 19th Australian Joint Conference on Artificial Intelligence - Hobart, Australia Duration: 4 Dec 2006 → 8 Dec 2006

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Number	4304
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	AI 2006: 19th Australian Joint Conference on Artificial Intelligence
City	Hobart, Australia
Period	4/12/06 → 8/12/06

Keywords

Pattern Recognition and Data Mining

Access to Document

10.1007/11941439_144

https://hdl.handle.net/1959.11/4606

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Cite this

@inproceedings{c0a468e08d9844048cb1029aed09f20e,

title = "Kernel Laplacian Eigenmaps for Visualization of Non-vectorial Data",

abstract = "In this paper, we propose the Kernel Laplacian Eigenmaps for nonlinear dimensionality reduction. This method can be extended to any structured input beyond the usual vectorial data, enabling the visualization of a wider range of data in low dimension once suitable kernels are defined. Comparison with related methods based on MNIST handwritten digits data set supported the claim of our approach. In addition to nonlinear dimensionality reduction, this approach makes visualization and related applications on non-vectorial data possible.",

keywords = "Pattern Recognition and Data Mining",

author = "Yi Guo and Junbin Gao and Kwan, \{Paul Hing\}",

year = "2006",

doi = "10.1007/11941439\_144",

language = "English",

isbn = "3540497870",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

number = "4304",

pages = "1179--1183",

editor = "A Sattar and BH Kang",

booktitle = "AI 2006: Advances in Artificial Intelligence",

note = "AI 2006: 19th Australian Joint Conference on Artificial Intelligence ; Conference date: 04-12-2006 Through 08-12-2006",

}

Guo, Y, Gao, J & Kwan, PH 2006, Kernel Laplacian Eigenmaps for Visualization of Non-vectorial Data. in A Sattar & BH Kang (eds), AI 2006: Advances in Artificial Intelligence. Lecture Notes in Computer Science, no. 4304, Springer, Berlin, Germany, pp. 1179-1183, AI 2006: 19th Australian Joint Conference on Artificial Intelligence, Hobart, Australia, 4/12/06. https://doi.org/10.1007/11941439_144

Kernel Laplacian Eigenmaps for Visualization of Non-vectorial Data. / Guo, Yi; Gao, Junbin; Kwan, Paul Hing.
AI 2006: Advances in Artificial Intelligence. ed. / A Sattar; BH Kang. Berlin, Germany: Springer, 2006. p. 1179-1183 (Lecture Notes in Computer Science; No. 4304).

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

TY - GEN

T1 - Kernel Laplacian Eigenmaps for Visualization of Non-vectorial Data

AU - Guo, Yi

AU - Gao, Junbin

AU - Kwan, Paul Hing

PY - 2006

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N2 - In this paper, we propose the Kernel Laplacian Eigenmaps for nonlinear dimensionality reduction. This method can be extended to any structured input beyond the usual vectorial data, enabling the visualization of a wider range of data in low dimension once suitable kernels are defined. Comparison with related methods based on MNIST handwritten digits data set supported the claim of our approach. In addition to nonlinear dimensionality reduction, this approach makes visualization and related applications on non-vectorial data possible.

AB - In this paper, we propose the Kernel Laplacian Eigenmaps for nonlinear dimensionality reduction. This method can be extended to any structured input beyond the usual vectorial data, enabling the visualization of a wider range of data in low dimension once suitable kernels are defined. Comparison with related methods based on MNIST handwritten digits data set supported the claim of our approach. In addition to nonlinear dimensionality reduction, this approach makes visualization and related applications on non-vectorial data possible.

KW - Pattern Recognition and Data Mining

U2 - 10.1007/11941439_144

DO - 10.1007/11941439_144

M3 - Conference contribution

SN - 3540497870

SN - 9783540497875

T3 - Lecture Notes in Computer Science

SP - 1179

EP - 1183

BT - AI 2006: Advances in Artificial Intelligence

A2 - Sattar, A

A2 - Kang, BH

PB - Springer

CY - Berlin, Germany

T2 - AI 2006: 19th Australian Joint Conference on Artificial Intelligence

Y2 - 4 December 2006 through 8 December 2006

ER -

Kernel Laplacian Eigenmaps for Visualization of Non-vectorial Data

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