Self-Paced Learning and Privileged Information based Cascaded Multi-column Classification algorithm for ASD diagnosis.

Autism spectrum disorder (ASD) is one of the most serious mental disorder in children. Machine learning based computer aided diagnosis (CAD) on resting-state functional magnetic resonance imaging (rs-fMRI) for ASD has attracted widespread attention. In recent years, learning using privileged information (LUPI), a supervised transfer learning method, has been generally used on multi-modality cases, which can transfer knowledge from source domain to target domain in order to improve the prediction capability on the target domain. However, multi-modality data is difficult to collect in clinical cases. LUPI method without introducing additional imaging modality images is worth further study. Random vector function link network plus (RVFL+) is a LUPI diagnosis algorithm, which has been proven to be effective for classification tasks. In this work, we proposed a self-paced learning based cascaded multi-column RVFL+ algorithm (SPL-cmcRVFL+) for ASD diagnosis. Initial classification model is trained using RVFL on the single-modal data (e.g. rs-fMRI). The output of the initial layer is then sent as privileged information (PI) to train the next layer of classification model. During this process, samples are selected using self-paced learning (SPL), which can adaptively select simple to difficult samples according to the loss value. The procedure is repeated until all samples are included. Experimental results show that our proposed method can accurately identify ASD and normal control, and outperforms other methods by a relatively higher classification accuracy.