Do Subsampled Newton Methods Work for High-Dimensional Data?

13 Feb 2019 Xiang Li Shusen Wang Zhihua Zhang

Subsampled Newton methods approximate Hessian matrices through subsampling techniques, alleviating the cost of forming Hessian matrices but using sufficient curvature information. However, previous results require $\Omega (d)$ samples to approximate Hessians, where $d$ is the dimension of data points, making it less practically feasible for high-dimensional data... (read more)

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