SPOT: A framework for selection of prototypes using optimal transport
This work addresses the need for human-understandable model and data summarization in machine learning applications, though it is incremental as it builds on existing optimal transport methods.
The authors tackled the problem of selecting representative prototypes from a dataset by developing an optimal transport-based framework that minimizes transport distance to the target distribution, resulting in an efficient greedy method with deterministic approximation guarantees validated on real-world benchmarks.
In this work, we develop an optimal transport (OT) based framework to select informative prototypical examples that best represent a given target dataset. Summarizing a given target dataset via representative examples is an important problem in several machine learning applications where human understanding of the learning models and underlying data distribution is essential for decision making. We model the prototype selection problem as learning a sparse (empirical) probability distribution having the minimum OT distance from the target distribution. The learned probability measure supported on the chosen prototypes directly corresponds to their importance in representing the target data. We show that our objective function enjoys a key property of submodularity and propose an efficient greedy method that is both computationally fast and possess deterministic approximation guarantees. Empirical results on several real world benchmarks illustrate the efficacy of our approach.