PCA Sparsified
From MaRDI portal
Publication:6176425
DOI10.1137/22m1492325OpenAlexW4385705510MaRDI QIDQ6176425
Fatih S. Aktaş, Mustafa Çelebi Pinar
Publication date: 23 August 2023
Published in: SIAM Journal on Optimization (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1137/22m1492325
Factor analysis and principal components; correspondence analysis (62H25) Numerical mathematical programming methods (65K05) Semidefinite programming (90C22)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
- Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions
- Smooth minimization of non-smooth functions
- The sparse principal component analysis problem: optimality conditions and algorithms
- Clustering and feature selection using sparse principal component analysis
- Convex approximations to sparse PCA via Lagrangian duality
- An exact approach to sparse principal component analysis
- An augmented Lagrangian approach for sparse principal component analysis
- Lectures on convex optimization
- Enhancing sparsity by reweighted \(\ell _{1}\) minimization
- On general minimax theorems
- Complexity bounds for primal-dual methods minimizing the model of objective function
- Provably optimal sparse solutions to overdetermined linear systems with non-negativity constraints in a least-squares sense by implicit enumeration
- Alternating maximization: unifying framework for 8 sparse PCA formulations and efficient parallel codes
- Certifiably optimal sparse principal component analysis
- Sparsistency and agnostic inference in sparse PCA
- Minimax sparse principal subspace estimation in high dimensions
- Multiplier and gradient methods
- Generalized power method for sparse principal component analysis
- On the $O(1/n)$ Convergence Rate of the Douglas–Rachford Alternating Direction Method
- Identifying small mean-reverting portfolios
- Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
- Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
- Updating the Inverse of a Matrix
- LAPACK Users' Guide
- Splitting Algorithms for the Sum of Two Nonlinear Operators
- Cones of Matrices and Set-Functions and 0–1 Optimization
- Estimating the Largest Eigenvalue by the Power and Lanczos Algorithms with a Random Start
- ARPACK Users' Guide
- First-Order Methods in Optimization
- Interior Point Methods in Semidefinite Programming with Applications to Combinatorial Optimization
- Conditional Gradient Algorithmsfor Rank-One Matrix Approximations with a Sparsity Constraint
- Scalable Semidefinite Programming
- A Direct Formulation for Sparse PCA Using Semidefinite Programming
- Optimization
- The direct extension of ADMM for multi-block convex minimization problems is not necessarily convergent