Sparse PCA: Convex Relaxations, Algorithms and Applications

DOI10.1007/978-1-4614-0769-0_31zbMath1334.90120arXiv1011.3781OpenAlexW1599867596MaRDI QIDQ2802550

No author found.

Publication date: 26 April 2016

Published in: International Series in Operations Research & Management Science (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1011.3781

zbMATH Keywords

sparse principal component analysisconvex relaxation techniques

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Semidefinite programming (90C22) Applications of mathematical programming (90C90)

Related Items (10)

An \(\ell_1\)-penalized adaptive normalized quasi-Newton algorithm for sparsity-aware generalized eigen-subspace tracking ⋮ Using ℓ1-Relaxation and Integer Programming to Obtain Dual Bounds for Sparse PCA ⋮ Solving sparse principal component analysis with global support ⋮ A Decomposition Augmented Lagrangian Method for Low-Rank Semidefinite Programming ⋮ Sparse PCA on fixed-rank matrices ⋮ Unnamed Item ⋮ A Sparse Decomposition of Low Rank Symmetric Positive Semidefinite Matrices ⋮ Alternating direction method of multipliers for penalized zero-variance discriminant analysis ⋮ Bayesian variable selection for globally sparse probabilistic PCA ⋮ Understanding large text corpora via sparse machine learning

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Testing the nullspace property using semidefinite programming
• The varimax criterion for analytic rotation in factor analysis
• High-dimensional analysis of semidefinite relaxations for sparse principal components
• Smoothing technique and its applications in semidefinite optimization
• Introductory lectures on convex optimization. A basic course.
• Low-Rank Approximations with Sparse Factors I: Basic Algorithms and Error Analysis
• Generalized power method for sparse principal component analysis
• Decoding by Linear Programming
• Matrix Analysis
• Cones of Matrices and Set-Functions and 0–1 Optimization
• Nineteen Dubious Ways to Compute the Exponential of a Matrix, Twenty-Five Years Later
• Interior Point Methods in Semidefinite Programming with Applications to Combinatorial Optimization
• Sparse Approximate Solutions to Linear Systems
• Sparse nonnegative solution of underdetermined linear equations by linear programming
• A Direct Formulation for Sparse PCA Using Semidefinite Programming

This page was built for publication: Sparse PCA: Convex Relaxations, Algorithms and Applications