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 40 articles )

Showing results 1 to 20 of 40.
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  1. Hao, Ruru; Su, Zhixun: A patch-based low-rank tensor approximation model for multiframe image denoising (2018)
  2. Bouwmans, Thierry; Sobral, Andrews; Javed, Sajid; Jung, Soon Ki; Zahzah, El-Hadi: Decomposition into low-rank plus additive matrices for background/foreground separation: a review for a comparative evaluation with a large-scale dataset (2017)
  3. Chow, Yat Tin; Darbon, Jér^ome; Osher, Stanley; Yin, Wotao: Algorithm for overcoming the curse of dimensionality for time-dependent non-convex Hamilton-Jacobi equations arising from optimal control and differential games problems (2017)
  4. Lee, Byungjoon; Darbon, Jér^ome; Osher, Stanley; Kang, Myungjoo: Revisiting the redistancing problem using the Hopf-Lax formula (2017)
  5. Jin, Zheng-Fen; Wan, Zhongping; Jiao, Yuling; Lu, Xiliang: An alternating direction method with continuation for nonconvex low rank minimization (2016)
  6. Ma, ShiQian; Yang, JunFeng: Applications of gauge duality in robust principal component analysis and semidefinite programming (2016)
  7. Tanner, Jared; Wei, Ke: Low rank matrix completion by alternating steepest descent methods (2016)
  8. Wei, Ke; Cai, Jian-Feng; Chan, Tony F.; Leung, Shingyu: Guarantees of Riemannian optimization for low rank matrix recovery (2016)
  9. Gao, Wei; Zhu, Linli; Wang, Kaiyun: Ontology sparse vector learning algorithm for ontology similarity measuring and ontology mapping via ADAL technology (2015)
  10. Hintermüller, Michael; Wu, Tao: Robust principal component pursuit via inexact alternating minimization on matrix manifolds (2015)
  11. Li, Yusheng; Xie, Xinchang; Yang, Zhouwang: Alternating direction method of multipliers for solving dictionary learning models (2015)
  12. Goldfarb, Donald; Qin, Zhiwei (Tony): Robust low-rank tensor recovery: models and algorithms (2014)
  13. Hage, Clemens; Kleinsteuber, Martin: Robust PCA and subspace tracking from incomplete observations using $\ell _0$-surrogates (2014)
  14. Kyrillidis, Anastasios; Cevher, Volkan: Matrix recipes for hard thresholding methods (2014)
  15. Liu, Guangcan; Yan, Shuicheng: Scalable low-rank representation (2014)
  16. Li, Yu-Fan; Zhang, Yan-Jiao; Huang, Zheng-Hai: A reweighted nuclear norm minimization algorithm for low rank matrix recovery (2014)
  17. Mishra, Bamdev; Meyer, Gilles; Bonnabel, Silvère; Sepulchre, Rodolphe: Fixed-rank matrix factorizations and Riemannian low-rank optimization (2014)
  18. Shen, Y.; Wen, Z.; Zhang, Y.: Augmented Lagrangian alternating direction method for matrix separation based on low-rank factorization (2014)
  19. Zhu, Kai; Wu, Rui; Ying, Lei; Srikant, R.: Collaborative filtering with information-rich and information-sparse entities (2014)
  20. Bi, Shujun; Han, Le; Pan, Shaohua: Approximation of rank function and its application to the nearest low-rank correlation matrix (2013)

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