Sparsity Promotion Models for the Choquet Integral
Mendez-Vazquez, A.
Gader, P.
Dept. of Comput. & Inf. Sci. & Eng., Florida Univ., Gainesville, FL;
This paper appears in: Foundations of Computational Intelligence, 2007. FOCI 2007. IEEE Symposium on
Publication Date: 1-5 April 2007
On page(s): 454-459
Location: Honolulu, HI,
ISBN: 1-4244-0703-6
INSPEC Accession Number: 9507018
Digital Object Identifier: 10.1109/FOCI.2007.371511
Current Version Published: 2007-06-18
Abstract
In this paper, we present a novel algorithm for learning fuzzy measures for Choquet integration. There are two novel aspects of the algorithm: it seeks to explicitly reduce the number of nonzero parameters in the measure to eliminate noninformative or useless information sources and it uses a Bayesian model for parameter estimation which has not been previously applied to the fuzzy measure learning problem. The method uses a hierarchical model that implements a sparsity promotion algorithm through a Gibbs sampler. This approach builds on the methods proposed by Figueiredo et al which uses expectation maximization (EM) to maximize the least absolute shrinkage and selection operator (LASSO) criterion under a distribution that promotes sparsity. Additional constraints are needed to satisfy the requirements of fuzzy measures. Figueiredo's algorithm does not have a mechanism for imposing these constraints. The constraints are imposed by sequentially exploring the lattice tree of the power set and requiring that each fuzzy measure value assigned to a set lies in the domain of a truncated Gaussian determined by the fuzzy measures of supersets of the set under consideration
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