Robust Unsupervised Multi-task and Transfer Learning on Gaussian Mixture Models

Understand

Unsupervised learning has been widely used in many real-world applications.

• One of the simplest and most important unsupervised learning models is the Gaussian mixture model (GMM).
• In this work, we study the multi-task learning problem on GMMs, which aims to leverage potentially similar GMM parameter structures among tasks to obtain improved learning performance compared to single-task learning.
• We propose a multi-task GMM learning procedure based on the EM algorithm that effectively utilizes unknown similarities between related tasks and is robust against a fraction of outlier tasks from arbitrary distributions.