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Decentralized Online Learning with Kernels. (arXiv:1710.04062v1 [math.OC])
来源于:arXiv
We consider multi-agent stochastic optimization problems over reproducing
kernel Hilbert spaces (RKHS). In this setting, a network of interconnected
agents aims to learn decision functions, i.e., nonlinear statistical models,
that are optimal in terms of a global convex functional that aggregates data
across the network, with only access to locally and sequentially observed
samples. We propose solving this problem by allowing each agent to learn a
local regression function while enforcing consensus constraints. We use a
penalized variant of functional stochastic gradient descent operating
simultaneously with low-dimensional subspace projections. These subspaces are
constructed greedily by applying orthogonal matching pursuit to the sequence of
kernel dictionaries and weights. By tuning the projection-induced bias, we
propose an algorithm that allows for each individual agent to learn, based upon
its locally observed data stream and message passing with its neighbors only, a
regression 查看全文>>