Distributed Ridge Regression with Feature Partitioning
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We develop a new distributed algorithm to solve the ridge regression problem with feature partitioning of the observation matrix. The proposed algorithm, named D-Ridge, is based on the alternating direction method of multipliers (ADMM) and estimates the parameters when the observation matrix is distributed among different agents with feature (or vertical) partitioning. We formulate the associated ridge regression problem as a distributed convex optimization problem and utilize the ADMM to obtain an iterative solution. Numerical results demonstrate that D-Ridge converges faster than its diffusion-based contender does.