Gaussian determinantal processes: A new model for directionality in data

DOI10.1073/pnas.1917151117zbMath1485.62074arXiv2111.09990OpenAlexW3029002575WikidataQ96124814 ScholiaQ96124814MaRDI QIDQ5073075

Subhroshekhar Ghosh, Philippe Rigollet

Publication date: 5 May 2022

Published in: Proceedings of the National Academy of Sciences (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2111.09990

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55)

Related Items (1)

The Poisson binomial distribution -- old \& new

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Optimal detection of sparse principal components in high dimension
• Some open problems in random matrix theory and the theory of integrable systems. II
• Characteristic vectors of bordered matrices with infinite dimensions
• On the distribution of the largest eigenvalue in principal components analysis
• The Dyson and Coulomb games
• Phase transition of the largest eigenvalue for nonnull complex sample covariance matrices
• Determinantal Point Processes for Machine Learning
• Principal component analysis: a review and recent developments
• Universality in numerical computations with random data
• The largest eigenvalues of sample covariance matrices for a spiked population: Diagonal case
• Multisurface method of pattern separation for medical diagnosis applied to breast cytology.
• Some open problems in random matrix theory and the theory of integrable systems
• The statistical properties of the city transport in Cuernavaca (Mexico) and random matrix ensembles
• Determinantal Point Process Models and Statistical Inference
• A model for the bus system in Cuernavaca (Mexico)
• Random Matrices in Physics
• Numerical inverting of matrices of high order
• Introduction to nonparametric estimation

This page was built for publication: Gaussian determinantal processes: A new model for directionality in data