Sparse Regression Incorporating Graphical Structure among Predictors.

ABSTRACT: With the abundance of high dimensional data in various disciplines, sparse regularized techniques are very popular these days. In this paper, we make use of the structure information among predictors to improve sparse regression models. Typically, such structure information can be modeled by the connectivity of an undirected graph using all predictors as nodes of the graph. Most existing methods use this undirected graph edge-by-edge to encourage the regression coefficients of corresponding connected predictors to be similar. However, such methods do not directly utilize the neighborhood information of the graph. Furthermore, if there are more edges in the predictor graph, the corresponding regularization term will be more complicate. In this paper, we incorporate the graph information nod

SUBMITTER: Yu G 

PROVIDER: S-EPMC5830184 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature