Abstract
The viewpoint that cancer is a genetic disease has been widely accepted by modern medicine. Therefore, seeking out the useful information from gene microarray data sets becomes a popular study. But because the data sets on gene expression have the features of small sample, high dimensionality and nonlinearity, traditional statistical methods face a challenge of “curse of dimensionality” and “problem of small sample size”. As a result, dimensionality reduction becomes the key to pattern recognition. This article uses Multidimensional Scaling and Laplacian Eigenmaps to reduce the dimensionality of the cancer data, and then uses Support Vector Machine to classify the data, Laplacian Eigenmaps achieving better result.
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Zuoling, L., Guirong, W. (2012). Dimensionality Reduction for Colon Data. In: Qian, Z., Cao, L., Su, W., Wang, T., Yang, H. (eds) Recent Advances in Computer Science and Information Engineering. Lecture Notes in Electrical Engineering, vol 124. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25781-0_21
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DOI: https://doi.org/10.1007/978-3-642-25781-0_21
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