DDAC-SpAM: A Distributed Algorithm for Fitting High-dimensional Sparse Additive Models with Feature Division and Decorrelation
This provides a practical solution for distributed statistical learning in high-dimensional settings, with applications across various domains, though it appears incremental as it builds on existing distributed methods by focusing on feature division rather than observation division.
The paper tackles the problem of fitting high-dimensional sparse additive models with large-scale data by proposing DDAC-SpAM, a distributed algorithm that divides features and uses decorrelation to recover sparsity patterns without strict correlation constraints, achieving consistent sparsity pattern recovery and statistical inference as demonstrated in theoretical and empirical results.
Distributed statistical learning has become a popular technique for large-scale data analysis. Most existing work in this area focuses on dividing the observations, but we propose a new algorithm, DDAC-SpAM, which divides the features under a high-dimensional sparse additive model. Our approach involves three steps: divide, decorrelate, and conquer. The decorrelation operation enables each local estimator to recover the sparsity pattern for each additive component without imposing strict constraints on the correlation structure among variables. The effectiveness and efficiency of the proposed algorithm are demonstrated through theoretical analysis and empirical results on both synthetic and real data. The theoretical results include both the consistent sparsity pattern recovery as well as statistical inference for each additive functional component. Our approach provides a practical solution for fitting sparse additive models, with promising applications in a wide range of domains.