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dc.contributor.authorKharal, Rosinaen 14:01:54 (GMT) 14:01:54 (GMT)
dc.description.abstractHarnessing 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.en
dc.format.extent2388818 bytes
dc.publisherUniversity of Waterlooen
dc.rightsCopyright: 2006, Kharal, Rosina . All rights reserved.en
dc.subjectComputer Scienceen
dc.subjectsemidefinite embeddingen
dc.subjectdimensionality reductionen
dc.subjectfeature selectionen
dc.subjectkernel alignmenten
dc.titleSemidefinite Embedding for the Dimensionality Reduction of DNA Microarray Dataen
dc.typeMaster Thesisen
dc.pendingfalseen of Computer Scienceen
uws-etd.degreeMaster of Mathematicsen

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