Pyglrm is a python package for modeling and fitting generalized low rank models (GLRMs), based on the Julia package LowRankModels.jl. GLRMs model a data array by a low rank matrix, and include many well known models in data analysis, such as principal components analysis (PCA), matrix completion, robust PCA, nonnegative matrix factorization, k-means, and many more.

References in zbMATH (referenced in 12 articles , 1 standard article )

Showing results 1 to 12 of 12.
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  1. Bai, Jushan; Ng, Serena: Rank regularized estimation of approximate factor models (2019)
  2. Driggs, Derek; Becker, Stephen; Aravkin, Aleksandr: Adapting regularized low-rank models for parallel architectures (2019)
  3. Gillis, Nicolas; Shitov, Yaroslav: Low-rank matrix approximation in the infinity norm (2019)
  4. Fithian, William; Mazumder, Rahul: Flexible low-rank statistical modeling with missing data and side information (2018)
  5. Liu, Lydia T.; Dobriban, Edgar; Singer, Amit: (e)PCA: high dimensional exponential family PCA (2018)
  6. Luo, Chongliang; Liang, Jian; Li, Gen; Wang, Fei; Zhang, Changshui; Dey, Dipak K.; Chen, Kun: Leveraging mixed and incomplete outcomes via reduced-rank modeling (2018)
  7. Yang, Lei; Pong, Ting Kei; Chen, Xiaojun: A nonmonotone alternating updating method for a class of matrix factorization problems (2018)
  8. Bigot, Jérémie; Deledalle, Charles; Féral, Delphine: Generalized SURE for optimal shrinkage of singular values in low-rank matrix denoising (2017)
  9. Dutta, Aritra; Li, Xin: On a problem of weighted low-rank approximation of matrices (2017)
  10. Fithian, William; Josse, Julie: Multiple correspondence analysis and the multilogit bilinear model (2017)
  11. Josse, Julie; Wager, Stefan: Bootstrap-based regularization for low-rank matrix estimation (2016)
  12. Udell, Madeleine; Horn, Corinne; Zadeh, Reza; Boyd, Stephen: Generalized low rank models (2016)