Data-Driven Model Reduction, Scientific Frontiers, and Applications
- Irina Gaynanova, Department of Statistics
- Structural Learning and Integrative Decomposition of Multi-View Data
Multi-view data, that is matched measurements from different sources on the same subjects, have become increasingly common with technological advances in genomics, neuroscience and wearable technologies to name a few. In this work we propose a new joint modeling and estimation framework for identification of latent components in these data, which can be subsequently used for exploratory dimension reduction, association analysis between the views, and consensus clustering. We illustrate the methodology on the breast cancer data from The Cancer Genome Atlas repository. This is joint work with Gen Li.