From this Wikipedia article, it looks like the key is the sparsity of data is problematic for statistical methods: http://en.wikipedia.org/wiki/Curse_of_dimens

From this Wikipedia article, it looks like the key is the sparsity of data is problematic for statistical methods: http://en.wikipedia.org/wiki/Curse_of_dimensionality http://en.wikipedia.org/wiki/Curse_of_dimensionality An alternative is to use multidimensional visualization techniques like parallel coordinates. This is a viable way to answer many questions and get a sense of the data, even if you're only working with sample. http://en.wikipedia.org/wiki/Parallel_coordinates http://en.wikipedia.org/wiki/Parallel_coordinates Questions like: Is this dimension ever relevant? Where are there strange outliers? How densely populated is the data? Do these dimensions have a relationship worth investigating? Of course you can answer most of these questions directly with code that computes the answer, but the advantage of high-dimensional visualization is it lets your visual system notice patterns which lead to questions you didn't think to ask.