Novelty detection, which aims to determine whether a given data belongs to any category of training data or not, is considered to be an important and challenging problem in areas of Pattern Recognition, Machine Learning, etc. Recently, kernel null space method (KNDA) was reported to have state-of-the-art performance in novelty detection. However, KNDA is hard to scale up because of its high computational cost. With the ever-increasing size of data, accelerating the implementing speed of KNDA is

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... desired and critical. Moreover, it becomes incapable when there exist successively injected data. To address these issues, we propose the Incremental Kernel Null Space based Discriminant Analysis (IKNDA) algorithm. The key idea is to extract new information brought by newly-added samples and integrate it with the existing model by an efficient updating scheme. Experiments conducted on two publicly-available datasets demonstrate that the proposed IKNDA yields comparable performance as the batch KNDA yet significantly reduces the computational complexity, and our IKNDA based novelty detection methods markedly outperform approaches using deep neural network (DNN) classifiers. This validates the superiority of our IKNDA against the state of the art in novelty detection for large-scale data.