2,992 research outputs found
Adaptation and learning over networks for nonlinear system modeling
In this chapter, we analyze nonlinear filtering problems in distributed
environments, e.g., sensor networks or peer-to-peer protocols. In these
scenarios, the agents in the environment receive measurements in a streaming
fashion, and they are required to estimate a common (nonlinear) model by
alternating local computations and communications with their neighbors. We
focus on the important distinction between single-task problems, where the
underlying model is common to all agents, and multitask problems, where each
agent might converge to a different model due to, e.g., spatial dependencies or
other factors. Currently, most of the literature on distributed learning in the
nonlinear case has focused on the single-task case, which may be a strong
limitation in real-world scenarios. After introducing the problem and reviewing
the existing approaches, we describe a simple kernel-based algorithm tailored
for the multitask case. We evaluate the proposal on a simulated benchmark task,
and we conclude by detailing currently open problems and lines of research.Comment: To be published as a chapter in `Adaptive Learning Methods for
Nonlinear System Modeling', Elsevier Publishing, Eds. D. Comminiello and J.C.
Principe (2018
Distributed Kernel Regression: An Algorithm for Training Collaboratively
This paper addresses the problem of distributed learning under communication
constraints, motivated by distributed signal processing in wireless sensor
networks and data mining with distributed databases. After formalizing a
general model for distributed learning, an algorithm for collaboratively
training regularized kernel least-squares regression estimators is derived.
Noting that the algorithm can be viewed as an application of successive
orthogonal projection algorithms, its convergence properties are investigated
and the statistical behavior of the estimator is discussed in a simplified
theoretical setting.Comment: To be presented at the 2006 IEEE Information Theory Workshop, Punta
del Este, Uruguay, March 13-17, 200
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