|
Abstract:
|
Multi-view data, that is matched sets of measurements on the same subjects, have become increasingly common with technological advances in genomics and other fields. Often, the subjects are separated into known classes, and it is of interest to find associations between the views that are related to the class membership. Existing classification methods can either be applied to each view separately or to the concatenated matrix of all views without taking into account between-views associations. On the other hand, existing association methods cannot directly incorporate class information. In this work, we propose a framework for Joint Association and Classification Analysis of multi-view data (JACA). In addition to joint learning framework, a distinct advantage of our approach is its ability to use partial information: it can be applied both in the settings with missing class labels and in the settings with missing subsets of views. We support the methodology with theoretical guarantees for estimation consistency in high-dimensional settings. Numerical studies on simulated and real data support the advantages of the proposed approach over existing methods.
|
