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

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. Outline. Often have little labeled data but lots of unlabeled data - PowerPoint PPT Presentation

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Page 1: Improving the Graph Mincut Approach to Learning from Labeled and Unlabeled Examples

Improving the Graph Mincut Approach to Learning from Labeled and Unlabeled

Examples

Avrim Blum, John Lafferty, Raja Reddy, Mugizi Rwebangira

Page 2: Improving the Graph Mincut Approach to Learning from Labeled and Unlabeled Examples

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

Page 3: Improving the Graph Mincut Approach to Learning from Labeled and Unlabeled Examples

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

Page 4: Improving the Graph Mincut Approach to Learning from Labeled and Unlabeled Examples

Construct a Graph

Page 5: Improving the Graph Mincut Approach to Learning from Labeled and Unlabeled Examples

Add sink and source

-+

Page 6: Improving the Graph Mincut Approach to Learning from Labeled and Unlabeled Examples

Obtain s-t mincut

Mincut

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Page 7: Improving the Graph Mincut Approach to Learning from Labeled and Unlabeled Examples

Classification

+ -

Mincut

Page 8: Improving the Graph Mincut Approach to Learning from Labeled and Unlabeled Examples

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

Page 9: Improving the Graph Mincut Approach to Learning from Labeled and Unlabeled Examples

Experiments

• Digits data set (each digit is a 16 X 16 integer array)

• 100 labeled examples

• 3900 unlabeled examples

• 100 runs of mincut

Page 10: Improving the Graph Mincut Approach to Learning from Labeled and Unlabeled Examples

Results

Page 11: Improving the Graph Mincut Approach to Learning from Labeled and Unlabeled Examples

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

Page 12: Improving the Graph Mincut Approach to Learning from Labeled and Unlabeled Examples

Questions?