NDLI: Serendipitous learning: learning beyond the predefined label space

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Serendipitous learning: learning beyond the predefined label space

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Serendipitous learning: learning beyond the predefined label space

Content Provider	ACM Digital Library
Author	Liu, Yan Si, Luo Zhang, Dan
Abstract	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.
Starting Page	1343
Ending Page	1351
Page Count	9
File Format	PDF
ISBN	9781450308137
DOI	10.1145/2020408.2020608
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-08-21
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Serendipitous learning Maximum margin classification Label space
Content Type	Text
Resource Type	Article

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