CAVIAR: CLASSIFICATION VIA AGGREGATED REGRESSION AND ITS APPLICATION IN CLASSIFYING OASIS BRAIN DATABASE.

This paper presents a novel classification via aggregated regression algorithm - dubbed CAVIAR - and its application to the OASIS MRI brain image database. The CAVIAR algorithm simultaneously combines a set of weak learners based on the assumption that the weight combination for the final strong hypothesis in CAVIAR depends on both the weak learners and the training data. A regularization scheme using the nearest neighbor method is imposed in the testing stage to avoid overfitting. A closed form solution to the cost function is derived for this algorithm. We use a novel feature - the histogram of the deformation field between the MRI brain scan and the atlas which captures the structural changes in the scan with respect to the atlas brain - and this allows us to automatically discriminate between various classes within OASIS [1] using CAVIAR. We empirically show that CAVIAR significantly increases the performance of the weak classifiers by showcasing the performance of our technique on OASIS.