Unsupervised Machine Learning: Application to Data Fusion

Fusion of information from multiple sets of data in order to extract a set of features that are most useful and relevant for the given task is inherent to many problems we deal with today. Data-driven methods based on source separation minimize the assumptions about the underlying relationships and enable fusion of information by letting multiple datasets to fully interact and inform each other. Use of multiple types of diversity - statistical property - enables maximal use of the available information when achieving source separation. In this talk, a number of powerful models are introduced for fusion of both multiset - data of the same nature - as well as multi-modal data, and the importance of diversity in fusion is demonstrated with a number of practical examples in medical imaging and video processing.

Speaker: Tulay Adali, Univ. of Maryland