Kernel based nonlinear dimensionality reduction for microarray gene expression data analysis

Accurate recognition of cancers based on microarray gene expressions is very important for doctors to choose a proper treatment. Genomic microarrays are powerful research tools in bioinformatics and modern medicinal research. 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. This paper proposed the kernel method based locally linear embedding to selecting the optimal number of nearest neighbors, constructing uniform distribution manifold. In this paper, a nonlinear dimensionality reduction kernel method based locally linear embedding is proposed to select the optimal number of nearest neighbors, constructing uniform distribution manifold. In addition, support vector machine which has given rise to the development of a new class of theoretically elegant learning machines will be used to classify and recognise genomic microarray. We demonstrate the application of the techniques to two published DNA microarray data sets. The experimental results and comparisons demonstrate that the proposed method is effective approach.

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