Deep MultiView Representation Learning for Multi modal Features of the Schizophrenia and Schizoaffective Disorder
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)