A0212
Title: Subset selection via continuous optimization
Authors: Samuel Muller - Macquarie University (Australia) [presenting]
Benoit Liquet - Macquary University (Australia)
Sarat Moka - The University of New South Wales (Australia)
Houying Zhu - Macquarie University (Australia)
Abstract: Recent rapid developments in information technology have enabled the collection of high-dimensional complex data, including in engineering, economics, finance, biology, and health sciences. High-dimensional means that the number of features is large and often far higher than the number of collected data samples. In many of these applications, it is desirable to find a small best subset of predictors so that the resulting model has desirable prediction accuracy. We will first briefly review existing optimization and search methods in the literature that tackle the problem of identifying or selecting the set of important predictors. We then present the COMBSS framework, a continuous optimization-based solution that we recently showed to solve the best subset selection problem in linear regression. Then, we focus on highlighting how COMBSS can be extended to other models. We explore how this is possible in generalized linear models, partial least-squares or principal component analysis.
