Simultaneous Clustering and Optimization for Evolving Datasets
This work addresses a computational bottleneck for researchers and practitioners dealing with dynamic data in machine learning, though it is incremental as it builds on existing SCO and ADMM methods.
The paper tackles the problem of simultaneous clustering and optimization for evolving datasets, where existing methods require frequent updates that are computationally infeasible, and proposes a new ADMM variant that achieves theoretical accuracy guarantees for tasks like ridge regression and convex clustering, with empirical studies confirming its effectiveness.
Simultaneous clustering and optimization (SCO) has recently drawn much attention due to its wide range of practical applications. Many methods have been previously proposed to solve this problem and obtain the optimal model. However, when a dataset evolves over time, those existing methods have to update the model frequently to guarantee accuracy; such updating is computationally infeasible. In this paper, we propose a new formulation of SCO to handle evolving datasets. Specifically, we propose a new variant of the alternating direction method of multipliers (ADMM) to solve this problem efficiently. The guarantee of model accuracy is analyzed theoretically for two specific tasks: ridge regression and convex clustering. Extensive empirical studies confirm the effectiveness of our method.