Document worth reading: “High Dimensional Classification via Empirical Risk Minimization: Improvements and Optimality”

In this textual content, we look at a family of classification algorithms outlined by the principle of empirical hazard minimization, throughout the extreme dimensional regime the place the attribute dimension $p$ and info amount $n$ are every big and comparable. Based on present advances in extreme dimensional statistics and random matrix idea, we provide beneath mixture info model a unified stochastic characterization of classifiers found with utterly totally different loss capabilities. Our outcomes are instrumental to an in-depth understanding along with smart enhancements on this primary classification methodology. As the first finish end result, we show the existence of a universally optimum loss carry out which yields the best extreme dimensional effectivity at any given $n/p$ ratio. High Dimensional Classification via Empirical Risk Minimization: Improvements and Optimality