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Enhancing Graph Mincut Learning with Confidence Scores

The study introduces a novel approach to learning with labeled and unlabeled examples by adding noise to edges in the graph mincut process to extract confidence scores for classifications. Experiments on a digits dataset show significant error reduction compared to standard mincut methods. Future work includes exploring different datasets and comparing with similar algorithms.

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Enhancing Graph Mincut Learning with Confidence Scores

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  1. Improving the Graph Mincut Approach to Learning from Labeled and Unlabeled Examples Avrim Blum, John Lafferty, Raja Reddy, Mugizi Rwebangira

  2. Outline • Often have little labeled data but lots of unlabeled data • Graph mincuts: based on a belief that most ‘close’ examples have same classification • Problem: -Does not say where it is most confident • Our approach: Add noise to edges to extract confidence scores

  3. Learning using Graph Mincuts:Blum and Chawla (ICML 2001)

  4. Construct a Graph

  5. Add sink and source + -

  6. Obtain s-t mincut + - Mincut

  7. Classification + - Mincut

  8. Goal • To obtain a measure of confidence on each classification Our approach • Add random noise to the edges • Run min cut several times • For each unlabeled example take majority vote

  9. Experiments • Digits data set (each digit is a 16 X 16 integer array) • 100 labeled examples • 3900 unlabeled examples • 100 runs of mincut

  10. Results

  11. Conclusions • 3% error on 80% of the data • Standard mincut only gives us 6% error on all the data • Future Work • Conduct further experiments on other data sets • Compare with similar algorithm of Jerry Zhu • Investigate the properties of the distribution we get by selecting minimum cuts in this way

  12. Questions?

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