Low-Rank Representation for Multi-center Autism Spectrum Disorder Identification.

Effective utilization of multi-center data for autism spectrum disorder (ASD) diagnosis recently has attracted increasing attention, since a large number of subjects from multiple centers are beneficial for investigating the pathological changes of ASD. To better utilize the multi-center data, various machine learning methods have been proposed. However, most previous studies do not consider the problem of data heterogeneity (e.g., caused by different scanning parameters and subject populations) among multi-center datasets, which may degrade the diagnosis performance based on multi-center data. To address this issue, we propose a multi-center low-rank representation learning (MCLRR) method for ASD diagnosis, to seek a good representation of subjects from different centers. Specifically, we first choose one center as the target domain and the remaining centers as source domains. We then learn a domain-specific projection for each source domain to transform them into an intermediate representation space. To further suppress the heterogeneity among multiple centers, we disassemble the learned projection matrices into a shared part and a sparse unique part. With the shared matrix, we can project target domain to the common latent space, and linearly represent the source domain datasets using data in the transformed target domain. Based on the learned low-rank representation, we employ the k-nearest neighbor (KNN) algorithm to perform disease classification. Our method has been evaluated on the ABIDE database, and the superior classification results demonstrate the effectiveness of our proposed method as compared to other methods.