Combining Autoencoder and Category-Based Low-Rank Domain Adaptation Method for Multi-Site ASD Identification

With the development of deep learning in diagnosing autism spectrum disorder (ASD), multi-site resting-state functional magnetic resonance imaging (rs-fMRI) images have made excellent progress. However, there is a heterogeneous problem between the multi-site data caused by inconsistent data distribution. To address this problem, we propose a combining autoencoder and category-based low-rank domain adaptation (AECLR) method for multi-site ASD identification. The main idea is to extract non-linear features and alignment the distribution of these features. In the first stage, the unsupervised autoencoder is used to obtain the non-linear representation. In the second stage, the common structure between all domains was mined by the category-based low-rank constraints, which transform all source domain data and the target domain data into the common latent space and then the source domain data could be linearly represented by the target domain data. As a result, the ablation experiments of the AECLR method achieve independent performance and the AECLR method also obtain a satisfactory classification when compared with the state-of-the-art method.