ECS 231 References

1. Elementary spectral graph theory applications to graph clustering
   A tutorial on the topic
   H. Chen
   S. Deng
   J. Wang
   W. Xing
   

2. Linear dimensionality reduction
   Survey the methods for linear dimension reduction from the perspective of optimization problems over matrix manifolds
   D. Roh

3. Chapter 2 of the book ``Generalized principal component analysis'' by R. Vidal, Y. Ma and S. Sastry, Spring 2016
   A review of classical theory of PCA, but with some modern twists and enrichment
   Y. Zhou
   S. Mu
   

4. Part III of Gilbert Strang's 2019 book ``Linear algebra and learning from data''
   Low rank approximation and compressed sensing
   M. Cheung
   

5. Deep Learning tutorial by Higham (focus on sections 1-6 first)
   C. He
   J. Wang
   Y. Liu
   B. Xiao
   X. Li
   J. Lin
   B. Xiao

6. Network properties revealed through matrix functions
   Z. Deng + N. Li

7. Randomized matrix-free trace and log-determinant estimators
   J. Stimac

8. Stochastic gradient descent, weighted sampling and the randomized Kaczmarz algorithm
   K. Patel