Class meeting for 10-405 Unsupervised Learning On Graphs
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Jump to navigationJump to searchThis is one of the class meetings on the schedule for the course Machine Learning with Large Datasets 10-405 in Spring 2018.
Slides
Quiz
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.
