Abstract: To multi-label sample, a class incremental learning algorithm based on hyper ellipsoidals was proposed. For every class, the smallest hyper ellipsoidal that contains most samples of the class was structured, which can divide the class samples from others. In the process of class incremental learning, the hyper cllipsoidals of new class were structured,and the historical hyper ellipsoidal that its class exists in the incremental samples was structured again. The multi-label class incremental learning is realized in a small memory space, and the history results that has nothing to do with the new sample classes arc saved at the same time. For the sample to be classified, its class is confirmed by the hyper ellipsoidal that it belongs to or its membership. The experimental results show that the algorithm has a higher performance on classification speed and classification precision compared with hyper sphere algorithm.

Key words: Hyper ellipsoidals, Multi-label, Incremental learning, Membership