Twenty Years of Mixture of Experts

• Published in: IEEE transaction on neural networks and learning systems Vol. 23; no. 8; pp. 1177 - 1193
• Main Authors: Yuksel, S. E., Wilson, J. N., Gader, P. D.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01-08-2012; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applications; Bayesian; Bayesian methods; Classification; comparison; Data models; Decision trees; Gaussian; Gaussian processes; Hidden Markov models; hierarchical mixture of experts (HME); Learning; Lists; mixture of Gaussian process experts; Neural networks; Regression; Regression analysis; statistical properties; Studies; Support vector machines; survey; Training; variational
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2012.2200299

In this paper, we provide a comprehensive survey of the mixture of experts (ME). We discuss the fundamental models for regression and classification and also their training with the expectation-maximization algorithm. We follow the discussion with improvements to the ME model and focus particularly on the mixtures of Gaussian process experts. We provide a review of the literature for other training methods, such as the alternative localized ME training, and cover the variational learning of ME in detail. In addition, we describe the model selection literature which encompasses finding the optimum number of experts, as well as the depth of the tree. We present the advances in ME in the classification area and present some issues concerning the classification model. We list the statistical properties of ME, discuss how the model has been modified over the years, compare ME to some popular algorithms, and list several applications. We conclude our survey with future directions and provide a list of publicly available datasets and a list of publicly available software that implement ME. Finally, we provide examples for regression and classification. We believe that the study described in this paper will provide quick access to the relevant literature for researchers and practitioners who would like to improve or use ME, and that it will stimulate further studies in ME.