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• W2938204024 abstract "In this paper, we develop a stochastic-gradient learning algorithm for situations involving streaming data that arise from an underlying clustered structure. In such settings, the variance of gradient noise can be decomposed into the in-cluster variance $sigma _{{text{in}}}^2$ plus the between- cluster variance $sigma _{{text{bet}}}^2$. We develop a cluster-based online variance- reduced method (COVER) to eliminate $sigma _{{text{bet}}}^2$ and improve the MSD performance of stochastic-gradient descent (SGD) to the order of $Oleft( {sigma _{{text{in}}}^2} right)$. We establish the convergence property of COVER and derive a tight closed-form mean-square deviation (MSD) performance expression. Our simulations illustrate the improved performance of COVER in terms of steady-state performance." @default.
• W2938204024 created "2019-04-25" @default.
• W2938204024 creator A5005538082 @default.
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• W2938204024 date "2019-05-01" @default.
• W2938204024 modified "2023-10-16" @default.
• W2938204024 title "COVER: A Cluster-based Variance Reduced Method for Online Learning" @default.
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• W2938204024 doi "https://doi.org/10.1109/icassp.2019.8682527" @default.
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