2020 IEEE International Symposium
on Information Theory
21-26 June 2020 • Los Angeles, California, USA
| Paper ID | L.2.2 | ||
| Paper Title | Optimality of Least-squares for Classification in Gaussian-Mixture Models | ||
| Authors | Hossein Taheri, Ramtin Pedarsani, Christos Thrampoulidis, University of California, Santa Barbara, United States | ||
| Session | L.2: Classification | ||
| Presentation | Lecture | ||
| Track | Statistics and Learning Theory | ||
| Manuscript | Click here to download the manuscript | ||
| Virtual Presentation | Click here to watch in the Virtual Symposium | ||
| Abstract | We consider the problem of learning the coefficients of a linear classifier through Empirical Risk Minimization with a convex loss function in the high-dimensional setting. In particular, we introduce an approach to characterize the best achievable classification risk among convex losses, when data points follow a standard Gaussian-mixture model. Importantly, we prove that the square loss function achieves the minimum classification risk for this data model. Our numerical illustrations verify the theoretical results and show that they are accurate even for relatively small problem dimensions. | ||
Plan Ahead
2021 IEEE International Symposium on Information Theory
11-16 July 2021 | Melbourne, Victoria, Australia
