document: Serendipitous learning : learning beyond the predefined label space Most traditional supervised learning methods are developed to learn a model from labeled examples and use this model to classify the unlabeled ones into the same label space predefined by the models . However , in many real world applications , the label spaces for both the labeled\/training and unlabeled\/testing examples can be different . To solve this problem , this paper proposes a novel notion of Serendipitous Learning ( SL ) , which is defined to address the learning scenarios in which the label space can be enlarged during the testing phase . In particular , a large margin approach is proposed to solve SL . The basic idea is to leverage the knowledge in the labeled examples to help identify novel\/unknown classes , and the large margin formulation is proposed to incorporate both the classification loss on the examples within the known categories , as well as the clustering loss on the examples in unknown categories . An efficient optimization algorithm based on CCCP and the bundle method is proposed to solve the optimization problem of the large margin formulation of SL . Moreover , an efficient online learning method is proposed to address the issue of large scale data in online learning scenario , which has been shown to have a guaranteed learning regret . An extensive set of experimental results on two synthetic datasets and two datasets from real world applications demonstrate the advantages of the proposed method over several other baseline algorithms . One limitation of the proposed method is that the number of unknown classes is given in advance . It may be possible to remove this constraint if we model it by using a non-parametric way . We also plan to do experiments on more real world applications in the future .
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