Learning Multiple Complex Features Based on Classification Results

Recently, methods for the unsupervised learning of features from large data sets have been attracting much attention. These methods have been especially successful in the area of computer vision. However, there is a problem that it is difficult to determine what kind of features will result in a high classification performance. Indeed, the difficulty of determining the learning parameters is a widely known problem in the field of feature learning. To address this problem, this paper presents a feature-learning method which uses classification results to progressively learn multiple features of varied complexity. The proposed method enables the learning of both simple robust features and complex features which represents difficult patterns. In addition, we assign regularization weights that are based on the complexity of the features. This modification emphasizes simple representation and prevents over fitting. Experimental results with medical image classification show that the proposed method is superior to the conventional method, especially when classification is difficult.