Sparse canonical correlation analysis
This work addresses a specific statistical problem for researchers dealing with high-dimensional data, but it is incremental as it builds on existing methods with added sparsity constraints.
The authors tackled the breakdown of classical canonical correlation analysis in high-dimensional settings by proposing a sparse version with l1 constraints on canonical vectors, demonstrating efficient solutions using linearized ADMM and TFOCS on simulated data.
Canonical correlation analysis was proposed by Hotelling [6] and it measures linear relationship between two multidimensional variables. In high dimensional setting, the classical canonical correlation analysis breaks down. We propose a sparse canonical correlation analysis by adding l1 constraints on the canonical vectors and show how to solve it efficiently using linearized alternating direction method of multipliers (ADMM) and using TFOCS as a black box. We illustrate this idea on simulated data.