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Sensors 2008, 8(7), 4186-4200; doi:10.3390/s8074186
Article

Kernel Based Nonlinear Dimensionality Reduction and Classification for Genomic Microarray

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Received: 4 June 2008 / Revised: 17 June 2008 / Accepted: 6 July 2008 / Published: 15 July 2008
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Abstract

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.
Keywords: Manifold learning; Dimensionality reduction; Locally linear embedding; Kernel methods; Support vector machine. Manifold learning; Dimensionality reduction; Locally linear embedding; Kernel methods; Support vector machine.
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Li, X.; Shu, L. Kernel Based Nonlinear Dimensionality Reduction and Classification for Genomic Microarray. Sensors 2008, 8, 4186-4200.

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