On the Feature Selection Criterion Proposed in ‘Gait Feature Subset Selection by Mutual Information’

Abstract Recently, Guo and Nixon [1] proposed a feature selection method based on maximizing I(x; Y ), the multidimensional mutual information between feature vector x and class variable Y . Because computing I(x; Y ) can be difficult in practice, Guo and Nixon proposed an approximation of I(x; Y ) as the criterion for feature selection. We show that Guo and Nixon’s criterion originates from approximating the joint probability distributions in I(x; Y ) by second-order product distributions. We remark on the limitations of the approximation and discuss alternatives to compute I(x; Y ) without sacrificing computational economy.