Incremental and robust learning of subspace representations

Learning is a fundamental capability of any cognitive system. To enable efficient operation of a cognitive agent in a real-world environment, visual learning has to be a continuous and robust process. In this article, we present a method for subspace learning, which takes these considerations into account. We present an incremental method, which sequentially updates the principal subspace considering weighted influence of individual images as well as individual pixels within an image. We further extend this approach to enable determination of consistencies in the input data and imputation of the inconsistent values using the previously acquired knowledge, resulting in a novel method for incremental, weighted, and robust subspace learning. We demonstrate the effectiveness of the proposed concept in several experiments on learning of object and background representations.