Sparse Canonical Correlation Analysis with Application to Genomic Data Integration

By E. Parkhomenko, D. Tritchler, Joseph Beyene. Statistical Applications in Genetics and Molecular Biology, 2009

364 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/80e2eaf558b2c6ccf9d1168399dca12a67961614

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

In Molecular Biology

Citations in the corpus

Canonical Correlation Analysis based on Hilbert-Schmidt Independence Criterion and Centered Kernel Target Alignment (2013, background): “More recently, the research community proposed regularized approaches to CCA for simultaneous analysis of multiple high-dimension data sets (Witten & Tibshirani, 2009; Parkhomenko et al., 2009; Hardoon & Shawe-Taylor, 2011).”
More Is Better: Recent Progress in Multi-Omics Data Integration Methods (2017, background): “L1-penalized sCCA (sparse CCA) together with elastic net CCA were proposed to ﬁlter the number of variables to make the results more biologically interpretable (Parkhomenko et al., 2009; Witten and Tibshirani, 2009).”
Random matrix theory in statistics: A review (2014, methodology): “…for the X and Y variables in the canonical correlation computation), are constrained to be sparse through imposition of sparse penalty, have also been proposed in the literature, notably by Hardoon and Shawe-Taylor (2009), Lee et al. (2009), Parkhomenko et al. (2009) and Witten et al. (2009).”
Sparse kernel canonical correlation analysis for discovery of nonlinear interactions in high-dimensional data (2017, methodology): “If we introduce sparse regularization on w and v , we obtain sparse projections [4, 6, 7].”
Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems (2011, methodology): “These penalties ( l 1 and/or l 2 ) were applied to the variable weight vectors in order to select the relevant variables in PCA [20,21] and more recently in Canonical Correlation Analysis [22-24] and in PLS [25-27].”
Bayesian group factor analysis with structured sparsity (2016, positive): “Jointly modeling sparse and dense factors effectively combines low-rank covariance factorization with interpretability (Zou et al., 2006; Parkhomenko et al., 2009).”