Statistics seminar - Professor Peter Radchenko - University of Sydney

A seminar by Professor Peter Radchenko from University of Sydney

Title: Modelling with categorical features via exact fusion and sparsity regularization

Abstract: We study the high-dimensional linear regression problem with categorical predictors that have many levels. We propose a new estimation approach, which performs model compression via two mechanisms by simultaneously encouraging (a) clustering of the regression coefficients to collapse some of the categorical levels together; and (b) sparsity of the regression coefficients.  We formulate our estimator as a solution to a mixed integer program and discuss ways to speed-up the computation. We also present a fast approximate algorithm for our method that obtains high-quality feasible solutions via block coordinate descent; the main building block of our algorithm is an exact solver for the univariate case.  We establish new theoretical guarantees for both the prediction and the cluster recovery performance of our estimator. Our numerical experiments on synthetic and real datasets demonstrate that our proposed estimator tends to outperform the state-of-the-art.

For further information, please contact RSFAS Seminars.

All information collected by the University is governed by the ANU Privacy Policy.