Full regularization path for sparse principal component analysis

By Alexandre d'Aspremont, Francis R. Bach, Laurent El Ghaoui. International Conference on Machine Learning, 2007

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Feature dimensionality reduction: a review (2022, background): “PCA allows the original complex variable to be represented by several integrated factors that reﬂect the information contained in the original variable as much as possible, and these factors do not relate to each other, to achieve the purpose of simpliﬁcation [48, 49].”
On learning and branching: a survey (2017, methodology): “This enforced limitation is motivated by the wish of further reducing the computational cost of features extraction, and by the fact that features change progressively along the tree, as it is shown by a 2-dimensional principal component analysis (PCA) (Abdi and Williams 2010).”
Sangerbox: A comprehensive, interaction‐friendly clinical bioinformatics analysis platform (2022, background): “In addition, some common bioanalysis processes, such as weighted correlation network analysis (WGCNA) [11], principal component analysis (PCA) [18], survival analysis [19], Gene Set Enrichment Analysis (GSEA) [20], and Limma differential expression analysis [21], are accommodated as well.”
A Comprehensive Survey of Loss Functions in Machine Learning (2020, background): “The specific algorithm flow can be found in [53].”
Towards Debiasing Sentence Representations (2020, methodology): “,vk} is given by the first k components of principal component analysis (PCA) (Abdi and Williams, 2010):”