Spectral Methods in Machine Learning Reading List

Curated by Mouhssine Rifaki | Stanford Electrical Engineering | Last updated April 2026

Eigenvalue decompositions and spectral techniques for learning, clustering, and dimensionality reduction.

1. A Tutorial on Spectral Clustering
   von Luxburg. Statistics and Computing 2007.
2. Spectral Methods for Data Science
   Chen, Fan, Ma, Yan. Cambridge 2024.
3. Matrix Perturbation Theory
   Stewart and Sun. Academic Press 1990.
4. Community Detection and Stochastic Block Models
   Abbe. Found. and Trends in Communications 2018.
5. Spectral Normalization for Generative Adversarial Networks
   Miyato et al.. ICLR 2018.
6. Spectral State Compression of Markov Processes
   Duan et al.. IEEE Control Systems Letters 2019.
7. Learning Continuous Control Policies by Stochastic Value Gradients
   Heess et al.. NeurIPS 2015.
8. Nonlinear Component Analysis as a Kernel Eigenvalue Problem
   Scholkopf et al.. Neural Computation 1998.
9. Randomized Numerical Linear Algebra: Foundations & Algorithms
   Martinsson and Tropp. Acta Numerica 2020.
10. Graph Neural Networks Exponentially Lose Expressive Power for Node Classification
    Oono and Suzuki. ICLR 2020.