Difference between revisions of "Class meeting for 10-605 Unsupervised Learning On Graphs"
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| − | This is one of the class meetings on the [[Syllabus for Machine Learning with Large Datasets 10-605 in Fall | + | This is one of the class meetings on the [[Syllabus for Machine Learning with Large Datasets 10-605 in Fall 2017|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Fall 2017]]. |
=== Slides === | === Slides === | ||
| − | * | + | * [http://www.cs.cmu.edu/~wcohen/10-605/unsup-on-graphs.pptx Powerpoint], [http://www.cs.cmu.edu/~wcohen/10-605/unsup-on-graphs.pdf PDF]. |
| − | === | + | === Quiz === |
| − | * | + | * There's no quiz today. |
| + | |||
| + | === Optional Readings === | ||
| + | |||
| + | * Von Luxburg, Ulrike. "A tutorial on spectral clustering." Statistics and computing 17.4 (2007): 395-416. | ||
| + | * Frank Lin and William W. Cohen (2010): Power Iteration Clustering in ICML-2010. | ||
| + | * Frank Lin and William W. Cohen (2010): A Very Fast Method for Clustering Big Text Datasets in ECAI-2010. | ||
| + | * Frank Lin and William W. Cohen (2011): Adaptation of Graph-Based Semi-Supervised Methods to Large-Scale Text Data in MLG-2011. | ||
| + | * Ramnath Balasubramanyan, Frank Lin, and William W. Cohen (2010): Node Clustering in Graphs: An Empirical Study in NIPS-2010 Workshop on Networks Across Disciplines. | ||
| + | |||
| + | === Things To Remember === | ||
| + | |||
| + | * The definitions of the graph Laplacian (D-A) and normalized Laplacian (I-W) | ||
| + | * What the largest eigenvectors of W look like for a block-stochastic matrix | ||
| + | * What spectral clustering is: clustering after mapping nodes in a graph to points defined by the largest K non-trivial eigenvectors of W. | ||
| + | * What power iteration clustering is. | ||
| + | * How to implement the "manifold trick" for PIC and SSL. | ||
| + | * Why the "manifold trick" improves computational efficiency, relative to computing a K-NN graph. | ||
Latest revision as of 13:04, 30 November 2017
This is one of the class meetings on the schedule for the course Machine Learning with Large Datasets 10-605 in Fall 2017.
Slides
Quiz
- There's no quiz today.
Optional Readings
- Von Luxburg, Ulrike. "A tutorial on spectral clustering." Statistics and computing 17.4 (2007): 395-416.
- Frank Lin and William W. Cohen (2010): Power Iteration Clustering in ICML-2010.
- Frank Lin and William W. Cohen (2010): A Very Fast Method for Clustering Big Text Datasets in ECAI-2010.
- Frank Lin and William W. Cohen (2011): Adaptation of Graph-Based Semi-Supervised Methods to Large-Scale Text Data in MLG-2011.
- Ramnath Balasubramanyan, Frank Lin, and William W. Cohen (2010): Node Clustering in Graphs: An Empirical Study in NIPS-2010 Workshop on Networks Across Disciplines.
Things To Remember
- The definitions of the graph Laplacian (D-A) and normalized Laplacian (I-W)
- What the largest eigenvectors of W look like for a block-stochastic matrix
- What spectral clustering is: clustering after mapping nodes in a graph to points defined by the largest K non-trivial eigenvectors of W.
- What power iteration clustering is.
- How to implement the "manifold trick" for PIC and SSL.
- Why the "manifold trick" improves computational efficiency, relative to computing a K-NN graph.
