Deep multi-view representation learning for multi-modal features of the schizophrenia and schizo-affective disorder

Abstract

This work is originated from the MLSP 2014 Classification Challenge which tries to automatically detect subjects with schizophrenia and schizo-affective disorder by analyzing multi-modal features derived from magnetic resonance imaging (MRI) data. We employ Deep Neural Network (DNN)-based multi-view representation learning for combining multimodal features. The DNN-based multi-view models include deep canonical correlation analysis (DCCA) and deep canonically correlated auto-encoders (DCCAE). In addition, support vector machine with Gaussian kernel is used to conduct classification with the compact bottleneck features learned by the deep multi-view models. Our experiments on the dataset provided by the MLSP Classification Challenge show that bottleneck features learned via deep multi-view models obtain better results than the trimming features used in the baseline system in terms of the receiver operating characteristic (ROC) area under the curve (AUC).