• PMA

  • Referenced in 106 articles [sw19126]
  • Analysis: a penalized matrix decomposition, sparse principal components analysis, and sparse canonical correlation analysis, described ... decomposition, with applications to sparse principal components and canonical correlation analysis. Biostatistics ... Witten and Tibshirani (2009) Extensions of sparse canonical correlation analysis, with applications to genomic data...
  • mixOmics

  • Referenced in 26 articles [sw09508]
  • Recent methodological developments include: sparse PLS-Discriminant Analysis, Independent Principal Component Analysis and multilevel analysis...
  • irlba

  • Referenced in 7 articles [sw21041]
  • Value Decomposition and Principal Components Analysis for Large Dense and Sparse Matrices. Fast and memory ... singular value decomposition and principal components analysis of large sparse and dense matrices...
  • SOFAR

  • Referenced in 4 articles [sw31665]
  • with sparse singular value decomposition, sparse principal component analysis, sparse factor analysis, and spare vector...
  • Optimal-SPCA

  • Referenced in 2 articles [sw31821]
  • Certifiably optimal sparse principal component analysis. This paper addresses the sparse principal component analysis (SPCA...
  • sparsepca

  • Referenced in 1 article [sw31562]
  • package sparsepca: Sparse Principal Component Analysis (SPCA). Sparse principal component analysis (SPCA) attempts to find ... approach provides better interpretability for the principal components in high-dimensional data settings. This...
  • GSPPCA

  • Referenced in 4 articles [sw25977]
  • globally sparse probabilistic PCA. Sparse versions of principal component analysis (PCA) have imposed themselves ... unsupervised manner. However, when several sparse principal components are computed, the interpretation of the selected...
  • spca_am

  • Referenced in 1 article [sw25022]
  • Sparse Principal Component Analysis Using Alternating Maximization, spca_am: This is a Sparse Principal Component...
  • scPCA

  • Referenced in 1 article [sw32451]
  • Bioconductor /R package scPCA: Sparse Contrastive Principal Component Analysis. A toolbox for sparse contrastive principal ... scPCA combines the stability and interpretability of sparse PCA with contrastive PCA’s ability...
  • s4vdpca

  • Referenced in 1 article [sw34285]
  • package implements methods for sparse principal component analysis as described in the Bioinformatics article: Applying ... Stability Selection to Consistently Estimate Sparse Principal Components in High-Dimensional Molecular Data...
  • Radio-iBAG

  • Referenced in 1 article [sw32135]
  • involves several innovative strategies: it incorporates integrative analysis of multi-platform genomic data sets ... also introduce the use of sparse Principal Component Analysis (sPCA) to extract a sparse...
  • SpaSM

  • Referenced in 4 articles [sw23903]
  • Matlab toolbox for performing sparse regression, classification and principal component analysis. The toolbox has been...
  • refund

  • Referenced in 45 articles [sw07434]
  • using principal components. Functional principal components (FPC) analysis is widely used to decompose and express ... implemented to understand the uncertainty in principal component decomposition quantities. Our method compares favorably ... simulation studies that include both densely and sparsely observed functions. We apply our method...
  • sparseFLMM

  • Referenced in 2 articles [sw21042]
  • irregularly or sparsely sampled data based on functional principal component analysis...
  • Bubbles

  • Referenced in 8 articles [sw08870]
  • independent component analysis, which can be estimated by maximization of the sparsenesses of linear filter ... outputs. This leads to the emergence of principal simple cell properties. Alternatively, simple cell properties...
  • PACE

  • Referenced in 2 articles [sw24632]
  • Functional Principal Component Analysis (FPCA), a key technique for functional data analysis, for sparsely ... random trajectories and time courses, via the Principal Analysis by Conditional Estimation (PACE) algorithm...
  • fdapace

  • Referenced in 2 articles [sw15968]
  • Functional Principal Component Analysis (FPCA), a key technique for functional data analysis, for sparsely ... random trajectories and time courses, via the Principal Analysis by Conditional Estimation (PACE) algorithm...
  • PACE

  • Referenced in 1 article [sw28690]
  • Functional Principal Component Analysis (FPCA), a key technique for functional data analysis, for sparsely ... random trajectories and time courses, via the Principal Analysis by Conditional Estimation (PACE) algorithm. PACE...
  • rpca

  • Referenced in 1 article [sw29201]
  • sparse component. Candes, E. J., Li, X., Ma, Y., & Wright, J. (2011). Robust principal component ... analysis?. Journal of the ACM (JACM), 58(3), 11. prove that we can recover each ... component individually under some suitable assumptions. It is possible to recover both the low-rank ... sparse components exactly by solving a very convenient convex program called Principal Component Pursuit; among...
  • dyndimred

  • Referenced in 1 article [sw33012]
  • reduction methods, such as Principal Component Analysis (’PCA’), Independent Component Analysis (’ICA’), diffusion maps, Locally ... Projection (’UMAP’). Has built-in support for sparse matrices...