LMaFit

LMaFit is a MATLAB package that currently solves the following problems: Matrix Complete (MC), Sparse Matrix Separation (SMS), Matrix Compressive Sensing (MCS).

Keywords for this software

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References in zbMATH (referenced in 36 articles )

Showing results 1 to 20 of 36.
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  1. Jin, Zheng-Fen; Wan, Zhongping; Jiao, Yuling; Lu, Xiliang: An alternating direction method with continuation for nonconvex low rank minimization (2016)
  2. Ma, ShiQian; Yang, JunFeng: Applications of gauge duality in robust principal component analysis and semidefinite programming (2016)
  3. Tanner, Jared; Wei, Ke: Low rank matrix completion by alternating steepest descent methods (2016)
  4. Wei, Ke; Cai, Jian-Feng; Chan, Tony F.; Leung, Shingyu: Guarantees of Riemannian optimization for low rank matrix recovery (2016)
  5. Gao, Wei; Zhu, Linli; Wang, Kaiyun: Ontology sparse vector learning algorithm for ontology similarity measuring and ontology mapping via ADAL technology (2015)
  6. Hintermüller, Michael; Wu, Tao: Robust principal component pursuit via inexact alternating minimization on matrix manifolds (2015)
  7. Li, Yusheng; Xie, Xinchang; Yang, Zhouwang: Alternating direction method of multipliers for solving dictionary learning models (2015)
  8. Goldfarb, Donald; Qin, Zhiwei (Tony): Robust low-rank tensor recovery: models and algorithms (2014)
  9. Hage, Clemens; Kleinsteuber, Martin: Robust PCA and subspace tracking from incomplete observations using $\ell _0$-surrogates (2014)
  10. Kyrillidis, Anastasios; Cevher, Volkan: Matrix recipes for hard thresholding methods (2014)
  11. Liu, Guangcan; Yan, Shuicheng: Scalable low-rank representation (2014)
  12. Li, Yu-Fan; Zhang, Yan-Jiao; Huang, Zheng-Hai: A reweighted nuclear norm minimization algorithm for low rank matrix recovery (2014)
  13. Mishra, Bamdev; Meyer, Gilles; Bonnabel, Silvère; Sepulchre, Rodolphe: Fixed-rank matrix factorizations and Riemannian low-rank optimization (2014)
  14. Shen, Y.; Wen, Z.; Zhang, Y.: Augmented Lagrangian alternating direction method for matrix separation based on low-rank factorization (2014)
  15. Zhu, Kai; Wu, Rui; Ying, Lei; Srikant, R.: Collaborative filtering with information-rich and information-sparse entities (2014)
  16. Bi, Shujun; Han, Le; Pan, Shaohua: Approximation of rank function and its application to the nearest low-rank correlation matrix (2013)
  17. Cai, Jian-Feng; Osher, Stanley: Fast singular value thresholding without singular value decomposition (2013)
  18. Li, Qingna; Yan, He; Wu, Leqin; Wang, Robert: Robust PCA for ground moving target indication in wide-area surveillance radar system (2013) ioport
  19. Liu, Yuanyuan; Jiao, L.C.; Shang, Fanhua: An efficient matrix factorization based low-rank representation for subspace clustering (2013)
  20. Liu, Yuanyuan; Jiao, L.C.; Shang, Fanhua: A fast tri-factorization method for low-rank matrix recovery and completion (2013)

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