no code implementations • 4 Apr 2024 • Xiwei Zhang, Tao Li
Furthermore, if the data streams also satisfy the RKHS persistence of excitation condition, i. e. there exists a fixed length of time period, such that each eigenvalue of the conditional expectation of the operators induced by the input data accumulated over every time period has a uniformly positive lower bound with respect to time, then the output of the algorithm is consistent with the unknown function in mean square.
no code implementations • 20 Mar 2023 • Tao Li, Xiwei Zhang
Moreover, we propose a decentralized online learning algorithm in RKHS based on non-stationary and non-independent online data streams, and prove that the algorithm is mean square and almost surely strongly consistent if the operators induced by the random input data satisfy the infinite-dimensional spatio-temporal persistence of excitation condition.
no code implementations • 7 Jun 2022 • Xiwei Zhang, Tao Li, Xiaozheng Fu
We study the decentralized online regularized linear regression algorithm over random time-varying graphs.
no code implementations • 22 Aug 2019 • Jiexiang Wang, Tao Li, Xiwei Zhang
Firstly, for the delay-free case, we show that the algorithm gains can be designed properly such that all nodes' estimates converge to the true parameter in mean square and almost surely if the observation matrices and communication graphs satisfy the stochastic spatiotemporal persistence of excitation condition.