2017/10/31 by Morteza Noshad Iranzad, Iranzad, Morteza Noshad, Alfred O. Hero +1 · 1 citation
Computer Science · #Distributed Sensor Networks and Detection Algorithms #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Information Theory (cs.IT) #Machine Learning (stat.ML) #Machine Learning and Data Classification
paper · pdf · doi:10.48550/arxiv.1710.11315
openalex publication_date 2017/10/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Meta learning of optimal classifier error rates allows an experimenter to empirically estimate the intrinsic ability of any estimator to discriminate between two populations, circumventing the difficult problem of estimating the optimal Bayes classifier. To this end we propose a weighted nearest neighbor (WNN) graph estimator for a tight bound on the Bayes classification error; the Henze-Penrose (HP) divergence. Similar to recently proposed HP estimators [berisha2016], the proposed estimator is non-parametric and does not require density estimation. However, unlike previous approaches the proposed estimator is rate-optimal, i.e., its mean squared estimation error (MSEE) decays to zero at the fastest possible rate of O(1/M+1/N) where M,N are the sample sizes of the respective populations. We illustrate the proposed WNN meta estimator for several simulated and real data sets.