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In this paper, we use a probabilistic model to estimate the number ofuncorrelated features in a large dataset. Our model allows for both pairwisefeature correlation (collinearity) and interdependency of multiple features(multicollinearity) and we use the probabilistic method to obtain upper andlower bounds of the same order, for the size of a feature set that exhibits lowcollinearity and low multicollinearity. We also prove an auxiliary resultregarding mutually good constrained sets that is of independent interest.

A Probabilistic Model for Data Redundancy in the Feature Domain