Learning from Data Heterogeneity: Algorithms and Applications

Nowadays, as an intrinsic property of big data, data heterogeneity can be seen in a variety of real-world applications, ranging from security to manufacturing, from healthcare to crowdsourcing. It refers to any inhomogeneity in the data, and can be present in a variety of forms, corresponding to different types of data heterogeneity, such as task/view/instance/oracle heterogeneity. As shown in previous work as well as our own work, learning from data heterogeneity not only helps people gain a better understanding of the large volume of data, but also provides a means to leverage such data for effective predictive modeling. In this paper, along with multiple real applications, we will briefly review state-of-the-art techniques for learning from data heterogeneity, and demonstrate their performance at addressing these real world problems.

Authors

• Jingrui He University of Illinois Urbana-Champaign

Keywords

• Machine Learning: Multi-instance/Multi-label/Multi-view learning
• Machine Learning: Time-series/Data Streams
• Machine Learning: Transfer, Adaptation, Multi-task Learning