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Oral
Classification from Pairwise Similarity and Unlabeled Data
Han Bao · Gang Niu · Masashi Sugiyama
Supervised learning needs a huge amount of labeled data, which can be a big bottleneck under the situation where there is a privacy concern or labeling cost is high. To overcome this problem, we propose a new weakly-supervised learning setting where only similar (S) data pairs (two examples belong to the same class) and unlabeled (U) data points are needed instead of fully labeled data, which is called SU classification. We show that an unbiased estimator of the classification risk can be obtained only from SU data, and the estimation error of its empirical risk minimizer achieves the optimal parametric convergence rate. Finally, we demonstrate the effectiveness of the proposed method through experiments.
Author Information
Han Bao (The University of Tokyo / RIKEN)
Gang Niu (RIKEN)
Masashi Sugiyama (RIKEN / The University of Tokyo)
Related Events (a corresponding poster, oral, or spotlight)
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2018 Poster: Classification from Pairwise Similarity and Unlabeled Data »
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