Fundamental limits of low-rank matrix estimation with diverging aspect ratios

Cites Work

Unnamed Item
Unnamed Item
Unnamed Item
Sparse principal component analysis and iterative thresholding
Optimal detection of sparse principal components in high dimension
The singular values and vectors of low rank perturbations of large rectangular random matrices
Statistical guarantees for the EM algorithm: from population to sample-based analysis
The eigenvalues and eigenvectors of finite, low rank perturbations of large random matrices
A spectral algorithm for learning mixture models
High-dimensional analysis of semidefinite relaxations for sparse principal components
Fundamental limits of symmetric low-rank matrix estimation
Optimality and sub-optimality of PCA. I: Spiked random matrix models
On the distribution of the largest eigenvalue in principal components analysis
Sharp optimal recovery in the two component Gaussian mixture model
Statistical limits of spiked tensor models
Testing in high-dimensional spiked models
When do birds of a feather flock together? \(k\)-means, proximity, and conic programming
The adaptive interpolation method: a simple scheme to prove replica formulas in Bayesian inference
Partial recovery bounds for clustering with the relaxed \(K\)-means
Computational and statistical boundaries for submatrix localization in a large noisy matrix
CHIME: clustering of high-dimensional Gaussian mixtures with EM algorithm and its optimality
Eigenvalues of large sample covariance matrices of spiked population models
Phase transition of the largest eigenvalue for nonnull complex sample covariance matrices
Estimation of low-rank matrices via approximate message passing
Testing for high-dimensional geometry in random graphs
Sparse PCA via Covariance Thresholding
Efficiently learning mixtures of two Gaussians
Generalized power method for sparse principal component analysis
Relax, No Need to Round
Improved Spectral-Norm Bounds for Clustering
Some inequalities satisfied by the quantities of information of Fisher and Shannon
Mutual Information and Minimum Mean-Square Error in Gaussian Channels
Isotropic PCA and Affine-Invariant Clustering
Community Detection and Stochastic Block Models
Submatrix localization via message passing
Asymptotic mutual information for the balanced binary stochastic block model
Clustering subgaussian mixtures by semidefinite programming
Information-Theoretic Bounds and Phase Transitions in Clustering, Sparse PCA, and Submatrix Localization
On Consistency and Sparsity for Principal Components Analysis in High Dimensions
The Dynamics of Message Passing on Dense Graphs, with Applications to Compressed Sensing
PAC Learning Axis-Aligned Mixtures of Gaussians with No Separation Assumption
Approximating K‐means‐type Clustering via Semidefinite Programming
Learning Theory
Polynomial Learning of Distribution Families
Contributions to the mathematical theory of evolution.
Spiked singular values and vectors under extreme aspect ratios
Mutual information for the sparse stochastic block model
Empirical Bayes PCA in high dimensions

This page was built for publication: Fundamental limits of low-rank matrix estimation with diverging aspect ratios