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Assessing the Number of Components in Finite Gaussian Mixtures by Generalised Fisher Ratio, Normalised Entropy Criterion and Functional Merging

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Abstract

This paper compares the performance of Generalised Fisher Ratio, Normalised Entropy and Functional Merging on assessing the exact parameters and number of components of finite Gaussian mixtures. The three methods use different statistical criteria for calculating the right number of components and have in common the Expectation-Maximisation algorithm for assessing the parameters of Gaussian mixtures. After applying the three above criteria to a similar benchmark as in paper (Celeux and Soromenho, Journal of Classification, 13, 1996) we show that Functional Merging surpass the performance of the other two methods.

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  1. Computer Science Department, Anglia Polytechnic University, East Road, Cambridge, CB1 1PT, England

    Christophe G. Molina

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  1. Christophe G. Molina
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Molina, C.G. Assessing the Number of Components in Finite Gaussian Mixtures by Generalised Fisher Ratio, Normalised Entropy Criterion and Functional Merging. The Journal of VLSI Signal Processing-Systems for Signal, Image, and Video Technology 26, 95–103 (2000). https://doi.org/10.1023/A:1008199518065

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