Decentralized EM to Learn Gaussian Mixtures from Datasets Distributed by Features. (arXiv:2201.09965v1 [cs.LG])

Web: http://arxiv.org/abs/2201.09965

Expectation Maximization (EM) is the standard method to learn Gaussian
mixtures. Yet its classic, centralized form is often infeasible, due to privacy
concerns and computational and communication bottlenecks. Prior work dealt with
data distributed by examples, horizontal partitioning, but we lack a
counterpart for data scattered by features, an increasingly common scheme (e.g.
user profiling with data from multiple entities). To fill this gap, we provide
an EM-based algorithm to fit Gaussian mixtures to Vertically Partitioned data
(VP-EM). In federated …

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