Alternating direction method of multipliers for penalized zero-variance discriminant analysis
From MaRDI portal
Publication:97537
DOI10.48550/arXiv.1401.5492zbMath1352.90072arXiv1401.5492OpenAlexW2273602044MaRDI QIDQ97537
Mingyi Hong, Brendan P. W. Ames, Brendan P. W. Ames, Mingyi Hong
Publication date: 21 January 2014
Published in: Computational Optimization and Applications (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1401.5492
classificationdimension reductionnonconvex optimizationalternating direction method of multipliersfeature selectionlinear discriminant analysis
Related Items
Perturbed proximal primal-dual algorithm for nonconvex nonsmooth optimization, The Proximal Alternating Direction Method of Multipliers in the Nonconvex Setting: Convergence Analysis and Rates, A Proximal Alternating Direction Method of Multiplier for Linearly Constrained Nonconvex Minimization, Structured nonconvex and nonsmooth optimization: algorithms and iteration complexity analysis, Decomposition Methods for Computing Directional Stationary Solutions of a Class of Nonsmooth Nonconvex Optimization Problems, Proximal Methods for Sparse Optimal Scoring and Discriminant Analysis, accSDA, Calculus of the exponent of Kurdyka-Łojasiewicz inequality and its applications to linear convergence of first-order methods, Convergence and rate analysis of a proximal linearized ADMM for nonconvex nonsmooth optimization, Alternating Direction Method of Multipliers for a Class of Nonconvex and Nonsmooth Problems with Applications to Background/Foreground Extraction
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis
- Regularized linear discriminant analysis and its application in microarrays
- Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
- Alternating direction method of multipliers for sparse principal component analysis
- Approximation bounds for sparse principal component analysis
- Sparse linear discriminant analysis by thresholding for high dimensional data
- Convex approximations to sparse PCA via Lagrangian duality
- Modified linear discriminant analysis approaches for classification of high-dimensional microarray data
- Improving implementation of linear discriminant analysis for the high dimension/small sample size problem
- On the Douglas-Rachford splitting method and the proximal point algorithm for maximal monotone operators
- Class prediction by nearest shrunken centroids, with applications to DNA microarrays.
- Some theory for Fisher's linear discriminant function, `naive Bayes', and some alternatives when there are many more variables than observations
- Penalized discriminant analysis
- Alternating maximization: unifying framework for 8 sparse PCA formulations and efficient parallel codes
- Eigenvalues of large sample covariance matrices of spiked population models
- Sparse PCA: Convex Relaxations, Algorithms and Applications
- Theory and applications of compressed sensing
- Generalized power method for sparse principal component analysis
- Convergence Analysis of Alternating Direction Method of Multipliers for a Family of Nonconvex Problems
- Penalized Classification using Fisher’s Linear Discriminant
- A Direct Estimation Approach to Sparse Linear Discriminant Analysis
- Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data
- Discriminant Analysis with Singular Covariance Matrices: Methods and Applications to Spectroscopic Data
- Regularization and Variable Selection Via the Elastic Net
- Model Selection and Estimation in Regression with Grouped Variables
- A Direct Formulation for Sparse PCA Using Semidefinite Programming
- Structured sparsity through convex optimization
