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As we move into higher dimensions, we will find even more corners. This will make an ever increasing percentage of the total space available.

Now imagine we have data spread across some multidimensional space. The higher the dimensionality, the higher the total proportion of our data will be “flung out” in the corners, and the more similar the distances will be between the minimum and maximum distances between points.

In higher dimensions our data are more sparse and more similarly spaced apart. This makes most distance functions less effective. Also, there other geometrical properties that varies in different dimensions.

Does these properties have any negative effect on SVM's performance for binary classiication?

If possible, please provide paper related to the issue.

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