A new distance measure for probability distribution function of mixture type

Authors:
Z. Liu;Q. Huang
Affiliations:
Electr. Eng. Dept., Polytech. Univ., Brooklyn, NY, USA;-
Venue:
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 01
Year:
2000

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Abstract

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.