Semi-supervised sparse representation classifier with random sample subset ensembles in fMRI-based brain state decoding

Multivariate classification techniques have been widely applied to decode brain states using functional magnetic resonance imaging (fMRI). Because the number of labeled samples is limited by the financial and safety consideration during fMRI data acquirement, it is not easy to train a robust classifier for fMRI data. Recently, semi-supervised learning has been proposed to train the classifier using both labeled training data and unlabeled data. Moreover, sparse representation based classification (SRC) has seldom been applied to fMRI data, although it exhibits a state-of-the-art classification performance in image processing. In this study, we proposed semi-supervised SRC with random sample subset ensemble strategy (semiSRC-RSSE) that used the average of class-specific coefficients as the SRC classification criterion and dynamically update the training dataset using the random sample subset ensemble method to measure the confidence of the prediction of each test sample. The results of the simulated and real fMRI experiments showed that semiSRC-RSSE method largely improved the classification accuracy of SRC and had better performance than support vector machine (SVM) and semi-supervised SVM with the random sample subset ensemble strategy (semiSVM-RSSE).