Semi-Supervised Learning with Very Few Labeled Training Examples

Zhi-Hua Zhou, De-Chuan Zhan, Qiang Yang

In semi-supervised learning, a number of labeled examples are usually required for training an initial weakly useful predictor which is in turn used for exploiting the unlabeled examples. However, in many real-world applications there may exist very few labeled training examples, which makes the weakly useful predictor difficult to generate, and therefore these semi-supervised learning methods cannot be applied. This paper proposes a method working under a two-view setting. By taking advantages of the correlations between the views using canonical component analysis, the proposed method can perform semi-supervised learning with only one labeled training example. Experiments and an application to content-based image retrieval validate the effectiveness of the proposed method.

Subjects: 12. Machine Learning and Discovery; 1.10 Information Retrieval

Submitted: Apr 9, 2007

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