| dc.description.abstract | Multi-view data, that is matched sets of measurements on the same subjects, have become
increasingly common with technological advances in genomics, neuroscience and wearable technologies, etc. Despite its prevalence, traditional techniques for classification or association analysis cannot be applied to multi-view data since they do not take into account the heterogeneity between the views. In this dissertation, we focus on generalizing the existing high-dimensional methods to multi-view data. First, we propose a framework for the Joint Association and Classification Analysis of multi-view data (JACA). We support the methodology with theoretical guarantees for estimation consistency in high-dimensional settings, and numerical comparisons with existing methods. In addition, our approach is capable of using partial information where class labels or subsets of views are missing. Second, we investigate the Pan-Cancer data with a goal to assess the strength of association between different cellular composition estimations by exploring the Generalized Association Study framework. We extract the shared and individual signals from each view, and evaluate the relationship they have with the survival to find out the bio-markers that are predictive for cancer prognosis. Lastly, we propose a low-rank canonical correlation analysis framework to model heterogeneous data (both Gaussian and non-Gaussian) using exponential family distributions. We exploit a decomposition-based strategy to extract shared and individual structures from underlying natural parameter matrices. In contrast to existing methods, our approach guarantees that there is no shared information embedded in the individual structures. An alternating split orthogonal constraints algorithm is developed to estimate the model parameters, and simulation studies show the advantages of the proposed approach over other classical methods. | en |
