Physical meaning of principal component analysis for classical lattice systems with translational invariance
arXiv:2410.22682 · doi:10.1103/PhysRevE.111.045301
Abstract
We explore the physical implications of applying principal component analysis (PCA) to translationally invariant classical systems defined on a -dimensional hypercubic lattice. Using Rayleigh-Schrödinger perturbation theory, we demonstrate that the principal components are related to the reciprocal lattice vectors of the hypercubic lattice, and the corresponding eigenvalues are connected to the discrete Fourier transform of the sampled configurations. From a different perspective, we show that the PCA in question can be viewed as a numerical method for computing the ensemble average of the squared moduli of the Fourier transform of physical quantities. Our results also provide a way to determine approximately the principal components of a classical system with translational invariance without the need for matrix diagonalization.
arXiv commentPublished in Phys. Rev. E
References in corpus (20)
- Machine learning and the physical sciences
- Nonequilibrium Critical Phenomena and Phase Transitions into Absorbing States
- Machine learning phases of matter
- A high-bias, low-variance introduction to Machine Learning for physicists
- Universality classes in nonequilibrium lattice systems
- Discovering Phase Transitions with Unsupervised Learning
- Unsupervised learning of phase transitions: from principal component analysis to variational autoencoders
- Discovering Phases, Phase Transitions and Crossovers through Unsupervised Machine Learning: A critical examination
- Quantum Loop Topography for Machine Learning
- Unsupervised machine learning account of magnetic transitions in the Hubbard model
- Unsupervised Learning of Frustrated Classical Spin Models I: Principle Component Analysis
- Quantifying hidden order out of equilibrium
- Machine Learning of Frustrated Classical Spin Models. II. Kernel Principal Component Analysis
- Supervised and unsupervised learning of directed percolation
- Quantitative analysis of phase transitions in two-dimensional XY models using persistent homology
- Visualizing Quantum Phases And Identifying Quantum Phase Transitions By Nonlinear Dimensionality Reduction
- Monte Carlo simulations of bosonic reaction-diffusion systems
- Principal component analysis of absorbing state phase transitions
- Monte Carlo simulations of bosonic reaction-diffusion systems and comparison to Langevin equation description
- Order-disorder transition in the two dimensional interacting monomer-dimer model: Ising criticality