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Generally in machine learning, it is desirable to work with low-dimensional feature vectors (FVs) to reduce computational complexity, and also to avoid the so-called curse of dimensionality phenomenon [2], which basically states that in high-dimensional representations the available data become too sparse for any decent statistical or structural analysis.
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