A Probabilistic Model for Data Redundancy in the Feature Domain
Ghurumuruhan Ganesan
In this paper, we use a probabilistic model to estimate the number of
uncorrelated features in a large dataset. Our model allows for both pairwise
feature correlation (collinearity) and interdependency of multiple features
(multicollinearity) and we use the probabilistic method to obtain upper and
lower bounds of the same order, for the size of a feature set that exhibits low
collinearity and low multicollinearity. We also prove an auxiliary result
regarding mutually good constrained sets that is of independent interest.