1 research outputs found
Meta Clustering for Collaborative Learning
An emerging number of learning scenarios involve a set of learners/analysts
each equipped with a unique dataset and algorithm, who may collaborate with
each other to enhance their learning performance. From the perspective of a
particular learner, a careless collaboration with task-irrelevant other
learners is likely to incur modeling error. A crucial problem is to search for
the most appropriate collaborators so that their data and modeling resources
can be effectively leveraged. Motivated by this, we propose to study the
problem of `meta clustering', where the goal is to identify subsets of relevant
learners whose collaboration will improve the performance of each individual
learner. In particular, we study the scenario where each learner is performing
a supervised regression, and the meta clustering aims to categorize the
underlying supervised relations (between responses and predictors) instead of
the raw data. We propose a general method named as Select-Exchange-Cluster
(SEC) for performing such a clustering. Our method is computationally efficient
as it does not require each learner to exchange their raw data. We prove that
the SEC method can accurately cluster the learners into appropriate
collaboration sets according to their underlying regression functions.
Synthetic and real data examples show the desired performance and wide
applicability of SEC to a variety of learning tasks