Supervised Fuzzy Mixture of Local Feature Models

Mingyang Xu, Michael Golay
DOI: 10.4236/iim.2011.33011                

Abstract

This paper addresses an important issue in model combination, that is, model locality. Since usually a global linear model is unable to reflect nonlinearity and to characterize local features, especially in a complex sys-tem, we propose a mixture of local feature models to overcome these weaknesses. The basic idea is to split the entire input space into operating domains, and a recently developed feature-based model combination method is applied to build local models for each region. To realize this idea, three steps are required, which include clustering, local modeling and model combination, governed by a single objective function. An adaptive fuzzy parametric clustering algorithm is proposed to divide the whole input space into operating regimes, local feature models are created in each individual region by applying a recently developed fea-ture-based model combination method, and finally they are combined into a single mixture model. Corre-spondingly, a three-stage procedure is designed to optimize the complete objective function, which is actu-ally a hybrid Genetic Algorithm (GA). Our simulation results show that the adaptive fuzzy mixture of local feature models turns out to be superior to global models.

Keywords

Adaptive Fuzzy Mixture, Supervised Clustering, Local Feature Model, PCA, ICA, Phase Transition, Fuzzy Parametric Clustering, Real-Coded Genetic Algorithm

Conflicts of Interest

The authors declare no conflicts of interest.

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