Author: Ali Punjani; Haowei Zhang; David J. Fleet
Title: Non-uniform refinement: Adaptive regularization improves single particle cryo-EM reconstruction Document date: 2019_12_16
ID: bqwmx5dy_12
Snippet: To that end, consider the use of 2-fold CV optimization to select the regularization operator, denoted r θ (m) in the regularization step in Algorithm 1 (note that k > 2 is also possible). The dataset D is partitioned into two halves, D 1 and D 2 , and two (unregularized) refinements are computed, namely m 1 and m 2 . For each, one half of the data is the 'training set', and the other is held out for validation. To find the regularizer parameter.....
Document: To that end, consider the use of 2-fold CV optimization to select the regularization operator, denoted r θ (m) in the regularization step in Algorithm 1 (note that k > 2 is also possible). The dataset D is partitioned into two halves, D 1 and D 2 , and two (unregularized) refinements are computed, namely m 1 and m 2 . For each, one half of the data is the 'training set', and the other is held out for validation. To find the regularizer parameters θ we wish to minimize the total CV error E, i.e.,
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