Sparse CCA using a Lasso with positivity constraints

Authors:
Anastasia Lykou;Joe Whittaker
Affiliations:
Department of Mathematics and Statistics, Lancaster University, Lancaster, LA1 4YF, UK;Department of Mathematics and Statistics, Lancaster University, Lancaster, LA1 4YF, UK
Venue:
Computational Statistics & Data Analysis
Year:
2010

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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.