Improving the Graph Mincut Approach to Learning from Labeled and Unlabeled Examples

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Improving the Graph Mincut Approach to Learning
from Labeled and Unlabeled Examples

• Avrim Blum, John Lafferty, Raja Reddy, Mugizi
  Rwebangira

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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

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Learning using Graph MincutsBlum and Chawla
(ICML 2001)
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Construct a Graph
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Add sink and source
-

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Obtain s-t mincut
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Mincut
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Classification

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Mincut
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• 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

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Experiments

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

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Results
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• 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

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Questions?