Distributed dictionary learning over a sensor network
This work addresses distributed learning for sensor networks, but it appears incremental as it adapts existing diffusion schemes to dictionary learning.
The paper tackles the problem of distributed dictionary learning across a sensor network by proposing a diffusion-based adaptive strategy where nodes share local dictionaries with neighbors, resulting in a distributed block coordinate descent algorithm. It demonstrates efficiency through numerical examples, though no concrete performance numbers are provided.
We consider the problem of distributed dictionary learning, where a set of nodes is required to collectively learn a common dictionary from noisy measurements. This approach may be useful in several contexts including sensor networks. Diffusion cooperation schemes have been proposed to solve the distributed linear regression problem. In this work we focus on a diffusion-based adaptive dictionary learning strategy: each node records observations and cooperates with its neighbors by sharing its local dictionary. The resulting algorithm corresponds to a distributed block coordinate descent (alternate optimization). Beyond dictionary learning, this strategy could be adapted to many matrix factorization problems and generalized to various settings. This article presents our approach and illustrates its efficiency on some numerical examples.