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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 isdoi:10.1109/cvpr.2017.439 dblp:conf/cvpr/LiuLWX17 fatcat:j5zrqvgg5rg5xjifz3rs43n7ke