Since SVMs do not restrict the dimension of the input data, the randomization results of the low-dimensional data can be compared to the high-dimensional original data, to see if more or less significant features got lost after the embedding.
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Comparative study of unsupervised dimension reduction techniques for the visualization of microarray gene expression data.
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