DICOD: Distributed Convolutional Sparse Coding
This work addresses the computational bottleneck in sparse coding for large-scale signal processing, though it is incremental as it builds on existing coordinate descent methods.
The paper tackles the problem of efficiently computing shift-invariant sparse representations for long signals by introducing DICOD, a distributed convolutional sparse coding algorithm that achieves super-linear speed-up with the number of cores compared to state-of-the-art methods.
In this paper, we introduce DICOD, a convolutional sparse coding algorithm which builds shift invariant representations for long signals. This algorithm is designed to run in a distributed setting, with local message passing, making it communication efficient. It is based on coordinate descent and uses locally greedy updates which accelerate the resolution compared to greedy coordinate selection. We prove the convergence of this algorithm and highlight its computational speed-up which is super-linear in the number of cores used. We also provide empirical evidence for the acceleration properties of our algorithm compared to state-of-the-art methods.