Semi-supervised Learning Based on Semiparametric Regularization

Description:

Semiparametric regularization based approach allows a family of algorithms to be developed based on various choices of the original RKHS and the loss function

Technology Overview:

Labeled data are often expensive to obtain since they require the efforts of experienced experts. Meanwhile, the unlabeled data are relatively easy to collect. Semiparametric regularization semi-supervised learning attempts to use the unlabeled data to improve the performance. Experimental comparisons demonstrate that our approach outperforms the state-of-the-art methods in the literature on a variety of classification tasks. Therefore, our approach is a promising technology in the machine learning field.

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Advantages:

In order to utilize the unlabeled data, the semiparametric regularization semi-supervised learning approach incorporates the marginal distribution of the data into the supervised learning through exploiting the geometric distribution of the data. This approach allows a family of algorithms to be developed based on various choices of the original RKHS and the loss function. Experimental comparisons demonstrate that the proposed approach outperforms the state-of-the-art methods in the literature on a variety of classification tasks.

Intellectual Property Summary: