Difference between revisions of "10-601 Matrix Factorization"
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| − | + | * What loss function and constraints are associated with PCA - i.e., what the "PCA optimization problem" is. | |
| − | * What loss function and constraints are associated with PCA - i.e., what the "PCA | ||
* How to interpret the low-dimensional embedding of instances, and the "prototypes" produced by PCA and MF techniques. | * 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 dimension reduction for images. | ||
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Revision as of 12:25, 13 April 2016
This a lecture used in the Syllabus for Machine Learning 10-601B in Spring 2016
Slides
Readings
- Murphy Chap 12; Bishop chapter 12. (PCA is not covered in Mitchell. )
- There are also some notes on PCA/SVD that I've written up.
- There's a good tutorial introduction to PCA on arxiv.
- 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 loss function and constraints are associated with PCA - i.e., what the "PCA optimization 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.
