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Graphical models have been widely applied in solving distributed inference problems in sensor networks. In this paper, the problem of coordinating a network of sensors to train a unique ensemble estimator under communication constraints is discussed. The information structure of graphical models with specific potential functions is employed, and this thus converts the collaborative training task into...
This paper introduces a modeling framework for distributed regression with agents/experts observing attribute-distributed data (heterogeneous data). Under this model, a new algorithm, the iterative covariance optimization algorithm (ICOA), is designed to reshape the covariance matrix of the training residuals of individual agents so that the linear combination of the individual estimators minimizes...
In this paper, an algorithm is developed for collaboratively training networks of kernel-linear least-squares regression estimators. The algorithm is shown to distributively solve a relaxation of the classical centralized least-squares regression problem. A statistical analysis shows that the generalization error afforded agents by the collaborative training algorithm can be bounded in terms of the...
This paper introduces a framework for regression with dimensionally distributed data with a fusion center. A cooperative learning algorithm, the iterative conditional expectation algorithm (ICEA), is designed within this framework. The algorithm can effectively discover linear combinations of individual estimators trained by each agent without transferring and storing large amount of data amongst...
In this paper, we discuss a local message passing algorithm for collaboratively training networks of kernel-linear least-squares regression estimators. The algorithm is constructed to solve a relaxation of the classical centralized kernel- linear least-squares regression problem. A statistical analysis shows that the generalization error afforded agents by the collaborative training algorithm can...
Distributed inference (e.g., detection, estimation, learning, etc.) is one of the primary applications of wireless sensor networks. This paper presents an overview of recent results by the author and co-workers in this area. The focus is on results in distributed learning and sensor scheduling, but some issues relating to energy efficiency are also discussed briefly
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