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 130 articles )

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  1. Saul, Lawrence K.: A nonlinear matrix decomposition for mining the zeros of sparse data (2022)
  2. Zhong, Yan; Huang, Jianhua Z.: Biclustering via structured regularized matrix decomposition (2022)
  3. Cai, Tony; Li, Hongzhe; Ma, Rong: Optimal structured principal subspace estimation: metric entropy and minimax rates (2021)
  4. Guo, Wenxing; Balakrishnan, Narayanaswamy; Bian, Mengjie: Reduced rank regression with matrix projections for high-dimensional multivariate linear regression model (2021)
  5. He, Di; Zhou, Yong; Zou, Hui: On sure screening with multiple responses (2021)
  6. Hu, Jian; Li, Mingyao: Discussion of “Exponential-family embedding with application to cell developmental trajectories for single-cell RNA-seq data” (2021)
  7. 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)
  8. Kawano, Shuichi: Sparse principal component regression via singular value decomposition approach (2021)
  9. Langworthy, Benjamin W.; Stephens, Rebecca L.; Gilmore, John H.; Fine, Jason P.: Canonical correlation analysis for elliptical copulas (2021)
  10. Richtárik, Peter; Jahani, Majid; Ahipaşaoğlu, Selin Damla; Takáč, Martin: Alternating maximization: unifying framework for 8 sparse PCA formulations and efficient parallel codes (2021)
  11. Risk, Benjamin B.; Gaynanova, Irina: Simultaneous non-Gaussian component analysis (SING) for data integration in neuroimaging (2021)
  12. Wang, Wei; Stephens, Matthew: Empirical Bayes matrix factorization (2021)
  13. Wang, Wenjia; Zhou, Yi-Hui: Eigenvector-based sparse canonical correlation analysis: fast computation for estimation of multiple canonical vectors (2021)
  14. Cai, Jia; Huo, Junyi: Sparse generalized canonical correlation analysis via linearized Bregman method (2020)
  15. Chi, Eric C.; Gaines, Brian J.; Sun, Will Wei; Zhou, Hua; Yang, Jian: Provable convex co-clustering of tensors (2020)
  16. Erichson, N. Benjamin; Zheng, Peng; Manohar, Krithika; Brunton, Steven L.; Kutz, J. Nathan; Aravkin, Aleksandr Y.: Sparse principal component analysis via variable projection (2020)
  17. Liu, Hongying; Wang, Hao; Song, Mengmeng: Projections onto the intersection of a one-norm ball or sphere and a two-norm ball or sphere (2020)
  18. Malec, Lukáš; Janovský, Vladimír: Connecting the multivariate partial least squares with canonical analysis: a path-following approach (2020)
  19. Ma, Zhuang; Li, Xiaodong: Subspace perspective on canonical correlation analysis: dimension reduction and minimax rates (2020)
  20. Mukhopadhyay, Minerva; Dunson, David B.: Targeted random projection for prediction from high-dimensional features (2020)

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