Title: Multitask diffusion adaptation over networks with common latent representations,
Abstract: 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 work
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.