10-601 PCA

From Cohen Courses
Jump to navigationJump to search

This a lecture used in the Syllabus for Machine Learning 10-601B in Spring 2016

Slides

Readings

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.