CDPA: common and distinctive pattern analysis between high-dimensional datasets
From MaRDI portal
Publication:2137801
DOI10.1214/22-EJS2008MaRDI QIDQ2137801
Publication date: 11 May 2022
Published in: Electronic Journal of Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1912.09989
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Angle-based joint and individual variation explained
- Concentration inequalities and moment bounds for sample covariance operators
- Regularized generalized canonical correlation analysis
- Sparsistency and rates of convergence in large covariance matrix estimation
- On the limit of the largest eigenvalue of the large dimensional sample covariance matrix
- Generalized Procrustes analysis
- Joint and individual variation explained (JIVE) for integrated analysis of multiple data types
- Asymptotics of empirical eigenstructure for high dimensional spiked covariance
- A fast projected fixed-point algorithm for large graph matching
- D-CCA: A Decomposition-Based Canonical Correlation Analysis for High-Dimensional Datasets
- Arbitrage, Factor Structure, and Mean-Variance Analysis on Large Asset Markets
- Rank tests of maximal power against Lehmann-type alternatives
- Literature survey on low rank approximation of matrices
- 10.1162/jmlr.2003.4.7-8.1261
- Numerical Methods for Computing Angles Between Linear Subspaces
- Why Are Big Data Matrices Approximately Low Rank?
- An iterative penalized least squares approach to sparse canonical correlation analysis
- A useful variant of the Davis–Kahan theorem for statisticians
- Canonical analysis of several sets of variables
- Large Covariance Estimation by Thresholding Principal Orthogonal Complements
- RELATIONS BETWEEN TWO SETS OF VARIATES
This page was built for publication: CDPA: common and distinctive pattern analysis between high-dimensional datasets
