LNNMLattice Neural Network Minimization
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The proposed methods, especially LNNM, consistently achieve comparable or smaller estimation errors even in the case of large quantity of missing data.
In this section, we illustrate some imputation examples of SM, NN, GNNM, and LNNM in case that PMV equals 10%, 30%, and 50% in Figures 2, 3, and 4, respectively.
In the second method (LNNM), we tend to perform nuclear norm minimization on those highly correlated samples for improving imputation performance.