PMA

R package PMA: Penalized Multivariate Analysis. Performs Penalized Multivariate Analysis: a penalized matrix decomposition, sparse principal components analysis, and sparse canonical correlation analysis, described in the following papers: (1) Witten, Tibshirani and Hastie (2009) A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. Biostatistics 10(3):515-534. (2) Witten and Tibshirani (2009) Extensions of sparse canonical correlation analysis, with applications to genomic data. Statistical Applications in Genetics and Molecular Biology 8(1): Article 28.


References in zbMATH (referenced in 136 articles )

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  1. Dey, Santanu S.; Mazumder, Rahul; Wang, Guanyi: Using (\ell_1)-relaxation and integer programming to obtain dual bounds for sparse PCA (2022)
  2. Frost, H. Robert: Eigenvectors from eigenvalues sparse principal component analysis (2022)
  3. Mao, Xianpeng; Yang, Yuning: Several approximation algorithms for sparse best rank-1 approximation to higher-order tensors (2022)
  4. Mao, Xianpeng; Yang, Yuning: Best sparse rank-1 approximation to higher-order tensors via a truncated exponential induced regularizer (2022)
  5. Polajnar, Emil: Using elastic net restricted kernel canonical correlation analysis for cross-language information retrieval (2022)
  6. Saul, Lawrence K.: A nonlinear matrix decomposition for mining the zeros of sparse data (2022)
  7. Zhong, Yan; Huang, Jianhua Z.: Biclustering via structured regularized matrix decomposition (2022)
  8. Cai, Tony; Li, Hongzhe; Ma, Rong: Optimal structured principal subspace estimation: metric entropy and minimax rates (2021)
  9. Guo, Wenxing; Balakrishnan, Narayanaswamy; Bian, Mengjie: Reduced rank regression with matrix projections for high-dimensional multivariate linear regression model (2021)
  10. He, Di; Zhou, Yong; Zou, Hui: On sure screening with multiple responses (2021)
  11. Hu, Jian; Li, Mingyao: Discussion of “Exponential-family embedding with application to cell developmental trajectories for single-cell RNA-seq data” (2021)
  12. Jiang, Haiyan; Xiong, Haoyi; Wu, Dongrui; Liu, Ji; Dou, Dejing: AgFlow: fast model selection of penalized PCA via implicit regularization effects of gradient flow (2021)
  13. Kawano, Shuichi: Sparse principal component regression via singular value decomposition approach (2021)
  14. Langworthy, Benjamin W.; Stephens, Rebecca L.; Gilmore, John H.; Fine, Jason P.: Canonical correlation analysis for elliptical copulas (2021)
  15. Richtárik, Peter; Jahani, Majid; Ahipaşaoğlu, Selin Damla; Takáč, Martin: Alternating maximization: unifying framework for 8 sparse PCA formulations and efficient parallel codes (2021)
  16. Risk, Benjamin B.; Gaynanova, Irina: Simultaneous non-Gaussian component analysis (SING) for data integration in neuroimaging (2021)
  17. Wang, Wei; Stephens, Matthew: Empirical Bayes matrix factorization (2021)
  18. Wang, Wenjia; Zhou, Yi-Hui: Eigenvector-based sparse canonical correlation analysis: fast computation for estimation of multiple canonical vectors (2021)
  19. Cai, Jia; Huo, Junyi: Sparse generalized canonical correlation analysis via linearized Bregman method (2020)
  20. Chi, Eric C.; Gaines, Brian J.; Sun, Will Wei; Zhou, Hua; Yang, Jian: Provable convex co-clustering of tensors (2020)

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