Majorization-Minimization on the Stiefel Manifold With Application to Robust Sparse PCA

DOI10.1109/TSP.2021.3058442OpenAlexW3131726128MaRDI QIDQ5103423

Author name not available (Why is that?)

Publication date: 23 September 2022

Published in: IEEE Transactions on Signal Processing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1109/tsp.2021.3058442

Mathematics Subject Classification ID

Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Related Items (4)

Riemannian Newton-type methods for joint diagonalization on the Stiefel manifold with application to independent component analysis ⋮ Robust principal component pursuit via inexact alternating minimization on matrix manifolds ⋮ A strengthened SDP relaxation for quadratic optimization over the Stiefel manifold ⋮ Min-max framework for majorization-minimization algorithms in signal processing applications: an overview

Recommendations

• Title not available (Why is that?) 👍 👎
• Minimax bounds for sparse PCA with noisy high-dimensional data 👍 👎
• Improve robustness of sparse PCA by \(L_{1}\)-norm maximization 👍 👎
• Two proposals for robust PCA using semidefinite programming 👍 👎
• Robust PCA using nonconvex rank approximation and sparse regularizer 👍 👎
• Robust principal component pursuit via inexact alternating minimization on matrix manifolds 👍 👎
• Sparse PCA: Convex Relaxations, Algorithms and Applications 👍 👎
• An alternating minimization method for robust principal component analysis 👍 👎
• 鲁棒的稀疏 Lp -模主成分分析 👍 👎

This page was built for publication: Majorization-Minimization on the Stiefel Manifold With Application to Robust Sparse PCA

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q5103423)