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Date of Award
Chandan K. Reddy
Integrating regularization methods within a regression framework has become a popular choice for researchers to build predictive models with lower variance and better generalization. Regularizers also aid in building interpretable models with high-dimensional data which makes them very appealing. Regularizers in general are unique in nature as they cater to data specific features such as correlation, structured sparsity, and temporal smoothness. The problem of obtaining a consensus among such diverse regularizers is extremely important in order to determine the optimal regularizer for the model. This is called the consensus regularization problem which has not received much attention in the literature, due to the inherent difficulty associated with building an integrated regularization framework. To solve this problem, in this thesis, we propose a method to generate a committee of non-convex regularized linear regression models, and use a consensus criterion to determine the optimal model for prediction. Each corresponding non-convex optimization problem in the committee is solved efficiently using the cyclic-coordinate descent algorithm with the generalized thresholding operator. Our Consensus RegularIzation Selection based Prediction (CRISP) model is evaluated on electronic health records (EHRs) obtained from a large hospital for the chronic heart failure readmission problem. We also evaluate our model on various synthetic datasets to assess its performance. The results indicate that CRISP outperforms several state-of-the-art methods such as additive models and other competing non-convex regularized linear regression methods.
Wang, Ping, "Consensus Regularized Selection Based Prediction" (2016). Wayne State University Theses. 511.