Distributed Dynamic Safe Screening Algorithms for Sparse Regularization
This work addresses the challenge of large-scale learning with massive samples and high-dimensional features for researchers and practitioners in machine learning, representing an incremental advance by extending safe screening to distributed architectures.
The paper tackles the problem of scaling safe screening for sparse regularization to distributed settings, proposing a distributed dynamic safe screening method that achieves significant speedup without accuracy loss, with theoretical guarantees of linear convergence and elimination of inactive features.
Distributed optimization has been widely used as one of the most efficient approaches for model training with massive samples. However, large-scale learning problems with both massive samples and high-dimensional features widely exist in the era of big data. Safe screening is a popular technique to speed up high-dimensional models by discarding the inactive features with zero coefficients. Nevertheless, existing safe screening methods are limited to the sequential setting. In this paper, we propose a new distributed dynamic safe screening (DDSS) method for sparsity regularized models and apply it on shared-memory and distributed-memory architecture respectively, which can achieve significant speedup without any loss of accuracy by simultaneously enjoying the sparsity of the model and dataset. To the best of our knowledge, this is the first work of distributed safe dynamic screening method. Theoretically, we prove that the proposed method achieves the linear convergence rate with lower overall complexity and can eliminate almost all the inactive features in a finite number of iterations almost surely. Finally, extensive experimental results on benchmark datasets confirm the superiority of our proposed method.