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I guess you have to look at dimensionality reduction of your problem, which is done in techniques like Fishers Linear Descriminant Analysis or Principal Component Analysis. They will help you discover the most influencing dimensions in your data, dimensions you wouldn't even notice at first sight.

Good examples that use these techniques are Fisherfaces and Eigenfaces. Have a look at it!