Semidefinite Embedding for the Dimensionality Reduction of DNA Microarray Data
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Date
2006
Authors
Kharal, Rosina
Journal Title
Journal ISSN
Volume Title
Publisher
University of Waterloo
Abstract
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.
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Keywords
Computer Science, semidefinite embedding, dimensionality reduction, feature selection, kernel alignment