Fig. 4
From: Joint deep learning for batch effect removal and classification toward MALDI MS based metabolomics
The architecture of the proposed deep learning framework for joint batch effect removal and classification. The source batch \({\mathbf{X}}_{1}\) and the target batch \({\mathbf{X}}_{2}\) are processed through the same calibrator \({\varvec{C}}\), such that both batches are compactly distributed in the latent space. The source batch supervises the training of the discriminator \({\varvec{D}}\), which then predicts the labels for the target batch in testing. Two reconstructors, \({\varvec{R}}_{1}\) and \({\varvec{R}}_{2}\), are used to ensure that the input data can be fully recovered from latent encoding