Multitask Diffusion Adaptation Over Networks With Common Latent Representations

Jie Chen, Cédric Richard, Ali H. Sayed

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56 引用 (Scopus)

摘要

Online learning with streaming data in a distributed and collaborative manner can be useful in a wide range of applications. This topic has been receiving considerable attention in recent years with emphasis on both single-task and multitask scenarios. In single-task adaptation, agents cooperate to track an objective of common interest, while in multitask adaptation agents track multiple objectives simultaneously. Regularization is one useful technique to promote and exploit similarity among tasks in the latter scenario. This paper examines an alternative way to model relations among tasks by assuming that they all share a common latent feature representation. As a result, a new multitask learning formulation is presented and algorithms are developed for its solution in a distributed online manner. We present a unified framework to analyze the mean-square-error performance of the adaptive strategies, and conduct simulations to illustrate the theoretical findings and potential applications.

源语言英语
文章编号7859344
页(从-至)563-579
页数17
期刊IEEE Journal on Selected Topics in Signal Processing
11
3
DOI
出版状态已出版 - 4月 2017

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