A new distance measure for probability distribution function of mixture type

Evaluating the similarity between two Probability Distribution Functions (PDF) is very important in various research problems. This paper proposes a new metric that computes the distance between two PDF's of mixture type directly from their parameters. It is posed as a linear programming problem and its theoretical properties and performance are analyzed, experimented, and compared with existing measures. In addition, as a proof of concept, we applied the new metric to the problem of audio retrieval where involved PDF's are GMM's (Gaussian Mixture Model) with 4 mixtures. Experimental results on both synthetic and real data show that this new distance measure is quite promising.