Minimax Quasi-Bayesian estimation in sparse canonical correlation analysis via a Rayleigh quotient function
This addresses a computational bottleneck for researchers and practitioners in fields like bioinformatics, using sparse CCA for applications such as correlating clinical and proteomic data in Covid-19 studies, but it is incremental as it builds on prior work with a Bayesian adaptation.
The paper tackles the challenge of estimating sparse canonical vectors in canonical correlation analysis (CCA), which has high computational cost in existing rate-optimal methods, by proposing a quasi-Bayesian estimation procedure that achieves the minimax estimation rate and is computationally efficient via MCMC, outperforming state-of-the-art methods in empirical tests.
Canonical correlation analysis (CCA) is a popular statistical technique for exploring relationships between datasets. In recent years, the estimation of sparse canonical vectors has emerged as an important but challenging variant of the CCA problem, with widespread applications. Unfortunately, existing rate-optimal estimators for sparse canonical vectors have high computational cost. We propose a quasi-Bayesian estimation procedure that not only achieves the minimax estimation rate, but also is easy to compute by Markov Chain Monte Carlo (MCMC). The method builds on Tan et al. (2018) and uses a re-scaled Rayleigh quotient function as the quasi-log-likelihood. However, unlike Tan et al. (2018), we adopt a Bayesian framework that combines this quasi-log-likelihood with a spike-and-slab prior to regularize the inference and promote sparsity. We investigate the empirical behavior of the proposed method on both continuous and truncated data, and we demonstrate that it outperforms several state-of-the-art methods. As an application, we use the proposed methodology to maximally correlate clinical variables and proteomic data for better understanding the Covid-19 disease.