Difference between revisions of "10-601 Matrix Factorization"
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=== Readings === | === Readings === | ||
| − | * PCA is not covered in Mitchell. | + | * Murphy Chap 12. PCA is not covered in Mitchell. |
* There are also some [http://www.cs.cmu.edu/~wcohen/10-601/PCA-notes/pca.pdf notes on PCA/SVD] that I've written up. | * There are also some [http://www.cs.cmu.edu/~wcohen/10-601/PCA-notes/pca.pdf notes on PCA/SVD] that I've written up. | ||
* There's a nice description of [http://people.mpi-inf.mpg.de/~rgemulla/publications/rj10481rev.pdf the gradient-based approach to MF], and a scheme for parallelizing it,by Gemulla et al. | * There's a nice description of [http://people.mpi-inf.mpg.de/~rgemulla/publications/rj10481rev.pdf the gradient-based approach to MF], and a scheme for parallelizing it,by Gemulla et al. | ||
Revision as of 16:59, 6 January 2016
This a lecture used in the Syllabus for Machine Learning 10-601B in Spring 2016
Slides
Readings
- Murphy Chap 12. PCA is not covered in Mitchell.
- There are also some notes on PCA/SVD that I've written up.
- There's a nice description of the gradient-based approach to MF, and a scheme for parallelizing it,by Gemulla et al.
Summary
You should know:
- What PCA is, and how it relates to matrix factorization.
- What loss function and constraints are associated with PCA - i.e., what the "PCA Problem" is.
- How to interpret the low-dimensional embedding of instances, and the "prototypes" produced by PCA and MF techniques.
- How to interpret the prototypes in the case of dimension reduction for images.
- How to interpret the prototypes in the case of collaborative filtering, and completion of a ratings matrix.
- How PCA and MF relate to k-means and and EM.
- The differences/similarities between PCA and SVD.
- The connection between SVD and LSI.
