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- W2253230487 abstract "We propose a framework to perform streaming covariance selection. Our approach employs regularization constraints where a time-varying sparsity parameter is iteratively estimated via stochastic gradient descent. This allows for the regularization parameter to be efficiently learnt in an online manner. The proposed framework is developed for linear regression models and extended to graphical models via neighbourhood selection. Under mild assumptions, we are able to obtain convergence results in a non-stochastic setting. The capabilities of such an approach are demonstrated using both synthetic data as well as neuroimaging data." @default.
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- W2253230487 date "2015-11-06" @default.
- W2253230487 modified "2023-09-27" @default.
- W2253230487 title "Streaming regularization parameter selection via stochastic gradient descent" @default.
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