Supplementary Material of " Algorithmic Stability and Generalization of an Unsupervised Feature Selection Algorithm "

Correspondence should be addressed to: qiang.cheng@uky.edu. The architecture of our algorithm is shown in Figure 1. For the training based on Eq. (2) of the main text, in each iteration of backpropagation, After training, only the trained selector is used to select features and do reconstruction during testing time. In Eq. (2) of the main text, the second term helps obtain During testing time, only the trained sub-NN is used to select features and do reconstruction. It has 5, 744 samples and 561 features.