Pages that link to "Item:Q2784376"
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The following pages link to Low-rank approximations with sparse factors. I: Basic algorithms and error analysis (Q2784376):
Displaying 16 items.
- Clustering and feature selection using sparse principal component analysis (Q374668) (← links)
- Finding a low-rank basis in a matrix subspace (Q517309) (← links)
- Convex approximations to sparse PCA via Lagrangian duality (Q631218) (← links)
- High-dimensional analysis of semidefinite relaxations for sparse principal components (Q834367) (← links)
- Solving \(\ell_0\)-penalized problems with simple constraints via the Frank-Wolfe reduced dimension method (Q2257078) (← links)
- Do semidefinite relaxations solve sparse PCA up to the information limit? (Q2352742) (← links)
- Approximate low-rank factorization with structured factors (Q2445796) (← links)
- Comments on ``An analytical algorithm for generalized low-rank approximations of matrices'' (Q2476983) (← links)
- Sparse PCA: Convex Relaxations, Algorithms and Applications (Q2802550) (← links)
- Robust lower rank approximation of matrices (Q3976288) (← links)
- Low-Rank PSD Approximation in Input-Sparsity Time (Q4575882) (← links)
- Low Rank Approximation of a Sparse Matrix Based on LU Factorization with Column and Row Tournament Pivoting (Q4610132) (← links)
- Less is More: Sparse Graph Mining with Compact Matrix Decomposition (Q4969614) (← links)
- Using ℓ1-Relaxation and Integer Programming to Obtain Dual Bounds for Sparse PCA (Q5095184) (← links)
- A proof of a conjecture of Gyárfás, Lehel, Sárközy and Schelp on Berge-cycles (Q5886104) (← links)
- Generalized low rank approximations of matrices (Q5896779) (← links)
