Transcript 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
Avrim Blum, John Lafferty, Raja Reddy,
Mugizi Rwebangira
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
Learning using Graph Mincuts:
Blum and Chawla (ICML 2001)
Construct a Graph
Add sink and source
+
-
Obtain s-t mincut
+
-
Mincut
Classification
+
-
Mincut
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
Experiments
• Digits data set (each digit is a 16 X 16
integer array)
• 100 labeled examples
• 3900 unlabeled examples
• 100 runs of mincut
Results
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
Questions?