A new distance measure for probability distribution function of mixture type
Z. Liu, Qian Huang · 2002
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 PDFs 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 PDFs are GMMs (Gaussian mixture model) with 4 mixtures. Experimental results on both synthetic and real data show that this new distance measure is quite promising.