Difference between revisions of "10-601 Matrix Factorization"
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** How to interpret the prototypes in the case of collaborative filtering, and completion of a ratings matrix. | ** 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. | * How PCA and MF relate to k-means and and EM. | ||
| + | |||
| + | * The differences/similarities between PCA and SVD. | ||
| + | * The connection between SVD and LSI. | ||
Revision as of 10:19, 17 November 2014
This a lecture used in the Syllabus for Machine Learning 10-601 in Fall 2014
Slides
Readings
- PCA is not covered in Mitchell. Bishop chapter 12 is optional reading.
- 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.
