首页|Exact fisher information of generalized Dirichlet multinomial distribution for count data modeling
Exact fisher information of generalized Dirichlet multinomial distribution for count data modeling
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NSTL
Elsevier
Despite the multiple benefits of the Fisher information matrix, it is generally disregarded and substituted by the identity matrix or an approximation format. However, when dealing with complicated real-world applications, ignoring the correlation between data features may compromise the modeling capability. To address this problem we present the exact calculation of the Fisher information matrix (EFIM) for the generalized Dirichlet multinomial (GDM) mixture that has proven its efficiency when modeling count data. We present a parametrization of GDM mixture model that allows the determination of the Fisher matrix's elements by means of the Beta-binomial probability function. We also propose a novel count data modeling approach with the benefit of EFIM. In particular, we tackle the problem of mixture model estimation and selection using the Fisher scoring algorithm and minimum message length within the Deterministic Annealing Expectation Maximization learning framework. Experiments on detecting depression in tweets, dialogue-based emotion recognition, and image-based sentiment analysis confirm the capability of the proposed approach and the merits of using the EFIM as compared with existing state-of-the-art methods and techniques that ignore the full determination of the Fisher information matrix's elements.(c) 2021 Elsevier Inc. All rights reserved.
Count data modelingGeneralized Dirichlet multinomialdistributionMixture modelsFisher information matrixFisher scoringMinimum message lengthMIXTUREMATRIXINDEPENDENCELIKELIHOOD
NSTL
Elsevier
