We address the problem of improving search efficiency of incremental variable selection. As one application, we focus on generalized linear models that are linear with respect to their parameters, but their objective functions are not restricted to a standard sum of squared error. In this paper, we present a method for incrementally selecting a set of relevant variables together with a newly proposing criterion based on second-order optimality for our models. In our experiments using a synthetic dataset with tens of thousands of variables, we show that the proposed method was able to completely restore the relevant variables. Moreover, the method substantially improved the search efficiency in comparison to a conventional calculation method. Furthermore, it is shown that we obtained promissing initial results using a real dataset in health-checkup.
展开▼
