Semidefinite Embedding for the DimensionalityReduction of DNA Microarray Data

Computer Science;semidefinite embedding;dimensionality reduction;feature selection;kernel alignment

Kharal, Rosina

University of Waterloo

关键词: Computer Science;    semidefinite embedding;    dimensionality reduction;    feature selection;    kernel alignment;   

Others  :  https://uwspace.uwaterloo.ca/bitstream/10012/2945/1/rkharal2006.pdf

瑞士|英语

来源: UWSPACE Waterloo Institutional Repository

【 摘 要 】

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, KAS (kernel alignment with semidefinite embedding), aids the visualization of microarray data in two dimensions and shows improvement over existing dimensionalityreduction methods such as PCA, LLE and Isomap.