Semi-supervised Learning Based on Semiparametric Regularization

 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.

 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:

  • U.S. 8,527,432 
    
    

    Additional Information:

    Inventor profile
    Multimedia Research Lab at SUNY Binghamton