Kernel Based Nonlinear Dimensionality Reduction and Classification for Genomic Microarray

• Main Authors: Lan Shu, Xuehua Li
• Format: Article
• Language: English
• Published: MDPI AG 2008-07-01
• Series: Sensors
• Subjects: Manifold learning; Dimensionality reduction; Locally linear embedding; Kernel methods; Support vector machine.
• Online Access: http://www.mdpi.com/1424-8220/8/7/4186/

Genomic microarrays are powerful research tools in bioinformatics and modern medicinal research because they enable massively-parallel assays and simultaneous monitoring of thousands of gene expression of biological samples. However, a simple microarray experiment often leads to very high-dimensional data and a huge amount of information, the vast amount of data challenges researchers into extracting the important features and reducing the high dimensionality. In this paper, a nonlinear dimensionality reduction kernel method based locally linear embedding(LLE) is proposed, and fuzzy K-nearest neighbors algorithm which denoises datasets will be introduced as a replacement to the classical LLE’s KNN algorithm. In addition, kernel method based support vector machine (SVM) will be used to classify genomic microarray data sets in this paper. We demonstrate the application of the techniques to two published DNA microarray data sets. The experimental results confirm the superiority and high success rates of the presented method.