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Semi-supervised Learning from General Unlabeled Data

11 years 6 months ago
Semi-supervised Learning from General Unlabeled Data
We consider the problem of Semi-supervised Learning (SSL) from general unlabeled data, which may contain irrelevant samples. Within the binary setting, our model manages to better utilize the information from unlabeled data by formulating them as a three-class (−1, +1, 0) mixture, where class 0 represents the irrelevant data. This distinguishes our work from the traditional SSL problem where unlabeled data are assumed to contain relevant samples only, either +1 or −1, which are forced to be the same as the given labeled samples. This work is also different from another family of popular models, universum learning (universum means “irrelevant” data), in that the universum need not to be specified beforehand. One significant contribution of our proposed framework is that such irrelevant samples can be automatically detected from the available unlabeled data, even though they are mixed with relevant data. This hence presents a general SSL framework that does not force “clean...
Kaizhu Huang, Zenglin Xu, Irwin King, Michael R. L
Added 30 May 2010
Updated 30 May 2010
Type Conference
Year 2008
Where ICDM
Authors Kaizhu Huang, Zenglin Xu, Irwin King, Michael R. Lyu
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