Semidefinite Embedding for the Dimensionality Reduction of DNA Microarray Data
Harnessing the power of DNA microarray technology requires the existence of analysis methods that accurately interpret microarray data. Current literature abounds with algorithms meant for the investigation of microarray data. However, there is need for an efficient approach that combines differ...
Main Author: | |
---|---|
Format: | Others |
Language: | en |
Published: |
University of Waterloo
2007
|
Subjects: | |
Online Access: | http://hdl.handle.net/10012/2945 |
id |
ndltd-WATERLOO-oai-uwspace.uwaterloo.ca-10012-2945 |
---|---|
record_format |
oai_dc |
spelling |
ndltd-WATERLOO-oai-uwspace.uwaterloo.ca-10012-29452013-01-08T18:50:13ZKharal, Rosina2007-05-08T14:01:54Z2007-05-08T14:01:54Z20062006http://hdl.handle.net/10012/2945Harnessing the power of DNA microarray technology requires the existence of analysis methods that accurately interpret microarray data. Current literature abounds with algorithms meant for the investigation of microarray data. However, there is need for an efficient approach that combines different techniques of microarray data analysis and provides a viable solution to dimensionality reduction of microarray data. Reducing the high dimensionality of microarray data is one approach in striving to better understand the information contained within the data. We propose a novel approach for dimensionality reduction of microarray data that effectively combines different techniques in the study of DNA microarrays. Our method, <strong><em>KAS</em></strong> (<em>kernel alignment with semidefinite embedding</em>), aids the visualization of microarray data in two dimensions and shows improvement over existing dimensionality reduction methods such as PCA, LLE and Isomap.application/pdf2388818 bytesapplication/pdfenUniversity of WaterlooCopyright: 2006, Kharal, Rosina . All rights reserved.Computer Sciencesemidefinite embeddingdimensionality reductionfeature selectionkernel alignmentSemidefinite Embedding for the Dimensionality Reduction of DNA Microarray DataThesis or DissertationSchool of Computer ScienceMaster of Mathematics |
collection |
NDLTD |
language |
en |
format |
Others
|
sources |
NDLTD |
topic |
Computer Science semidefinite embedding dimensionality reduction feature selection kernel alignment |
spellingShingle |
Computer Science semidefinite embedding dimensionality reduction feature selection kernel alignment Kharal, Rosina Semidefinite Embedding for the Dimensionality Reduction of DNA Microarray Data |
description |
Harnessing the power of DNA microarray technology requires the existence of analysis methods that accurately interpret microarray data. Current literature abounds with algorithms meant for the investigation of microarray data. However, there is need for an efficient approach that combines different techniques of microarray data analysis and provides a viable solution to dimensionality reduction of microarray data. Reducing the high dimensionality of microarray data is one approach in striving to better understand the information contained within the data. We propose a novel approach for dimensionality reduction of microarray data that effectively combines different techniques in the study of DNA microarrays. Our method, <strong><em>KAS</em></strong> (<em>kernel alignment with semidefinite embedding</em>), aids the visualization of microarray data in two dimensions and shows improvement over existing dimensionality reduction methods such as PCA, LLE and Isomap. |
author |
Kharal, Rosina |
author_facet |
Kharal, Rosina |
author_sort |
Kharal, Rosina |
title |
Semidefinite Embedding for the Dimensionality
Reduction of DNA Microarray Data |
title_short |
Semidefinite Embedding for the Dimensionality
Reduction of DNA Microarray Data |
title_full |
Semidefinite Embedding for the Dimensionality
Reduction of DNA Microarray Data |
title_fullStr |
Semidefinite Embedding for the Dimensionality
Reduction of DNA Microarray Data |
title_full_unstemmed |
Semidefinite Embedding for the Dimensionality
Reduction of DNA Microarray Data |
title_sort |
semidefinite embedding for the dimensionality
reduction of dna microarray data |
publisher |
University of Waterloo |
publishDate |
2007 |
url |
http://hdl.handle.net/10012/2945 |
work_keys_str_mv |
AT kharalrosina semidefiniteembeddingforthedimensionalityreductionofdnamicroarraydata |
_version_ |
1716572896079380480 |