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Sparse CCA using a Lasso with positivity constraints

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  • Lykou, Anastasia
  • Whittaker, Joe

Abstract

Canonical correlation analysis (CCA) describes the relationship between two sets of variables by finding linear combinations of the variables with maximal correlation. A sparse version of CCA is proposed that reduces the chance of including unimportant variables in the canonical variates and thus improves their interpretation. A version of the Lasso algorithm incorporating positivity constraints is implemented in tandem with alternating least squares (ALS), to obtain sparse canonical variates. The proposed method is demonstrated on simulation studies and a data set from market basket analysis.

Suggested Citation

  • Lykou, Anastasia & Whittaker, Joe, 2010. "Sparse CCA using a Lasso with positivity constraints," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3144-3157, December.
    • Handle: RePEc:eee:csdana:v:54:y:2010:i:12:p:3144-3157
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    References listed on IDEAS

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    1. Szefer Elena & Lu Donghuan & Nathoo Farouk & Beg Mirza Faisal & Graham Jinko, 2017. "Multivariate association between single-nucleotide polymorphisms in Alzgene linkage regions and structural changes in the brain: discovery, refinement and validation," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 16(5-6), pages 367-386, December.
    2. Dmitry Kobak & Yves Bernaerts & Marissa A. Weis & Federico Scala & Andreas S. Tolias & Philipp Berens, 2021. "Sparse reduced‐rank regression for exploratory visualisation of paired multivariate data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(4), pages 980-1000, August.

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