Dimension Reduction of Microarray Data Based on Local Principal Component

Anaissi, A; Kennedy, PJ; Goyal, ML

Dimension Reduction of Microarray Data Based on Local Principal Component

Anaissi, A Kennedy, PJ

Goyal, ML

Permalink

Publisher:: World Academy of Science, Engineering and Technology - WASET
Publication Type:: Journal Article
Citation:: World Academy of Science, Engineering and Technology, 2011, 77 pp. 68 - 73
Issue Date:: 2011-01

Closed Access

	Filename	Description	Size
	2010005351OK.pdf		422.25 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Anaissi, A	en_US
dc.contributor.author	Kennedy, PJ https://orcid.org/0000-0001-7837-3171	en_US
dc.contributor.author	Goyal, ML https://orcid.org/0000-0003-2853-9393	en_US
dc.contributor.editor	Vaninsky, A	en_US
dc.contributor.editor	Grecos, C	en_US
dc.contributor.editor	Mahanti, PK	en_US
dc.date.issued	2011-01	en_US
dc.identifier.citation	World Academy of Science, Engineering and Technology, 2011, 77 pp. 68 - 73	en_US
dc.identifier.issn	2010-3778	en_US
dc.identifier.uri	http://hdl.handle.net/10453/18228
dc.description.abstract	Analysis and visualization of microarraydata is veryassistantfor biologists and clinicians in the field of diagnosis and treatment of patients. It allows Clinicians to better understand the structure of microarray and facilitates understanding gene expression in cells. However, microarray dataset is a complex data set and has thousands of features and a very small number of observations. This very high dimensional data set often contains some noise, non-useful information and a small number of relevant features for disease or genotype. This paper proposes a non-linear dimensionality reduction algorithm Local Principal Component (LPC) which aims to maps high dimensional data to a lower dimensional space. The reduced data represents the most important variables underlying the original data. Experimental results and comparisons are presented to show the quality of the proposed algorithm. Moreover, experiments also show how this algorithm reduces high dimensional data whilst preserving the neighbourhoods of the points in the low dimensional space as in the high dimensional space.	en_US
dc.publisher	World Academy of Science, Engineering and Technology - WASET	en_US
dc.relation.ispartof	World Academy of Science, Engineering and Technology	en_US
dc.title	Dimension Reduction of Microarray Data Based on Local Principal Component	en_US
dc.type	Journal Article
utslib.citation.volume	77	en_US
utslib.location.activity	france	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	france	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access
pubs.consider-herdc	true	en_US
pubs.volume	77	en_US

Abstract:

Analysis and visualization of microarraydata is veryassistantfor biologists and clinicians in the field of diagnosis and treatment of patients. It allows Clinicians to better understand the structure of microarray and facilitates understanding gene expression in cells. However, microarray dataset is a complex data set and has thousands of features and a very small number of observations. This very high dimensional data set often contains some noise, non-useful information and a small number of relevant features for disease or genotype. This paper proposes a non-linear dimensionality reduction algorithm Local Principal Component (LPC) which aims to maps high dimensional data to a lower dimensional space. The reduced data represents the most important variables underlying the original data. Experimental results and comparisons are presented to show the quality of the proposed algorithm. Moreover, experiments also show how this algorithm reduces high dimensional data whilst preserving the neighbourhoods of the points in the low dimensional space as in the high dimensional space.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/18228