2016/10/21 by Tianpei Xie, Xie, Tianpei, Nasser. M. Narabadi +3
Computer Science · Mathematics · #Artificial intelligence #Computer science #FOS: Computer and information sciences #Face and Expression Recognition #Geography #H.1.1 #I.1.2 #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Mathematics #Meteorology #Neural Networks and Applications #Set (abstract data type) #Training (meteorology) #Training set #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1610.06806
published in arXiv (Cornell University) (Cornell University) · 13 pages; Accepted in Transaction on Signal Processing, 2016. arXiv admin note: text overlap with arXiv:1507.04540
arxiv created 2016/10/21 · openalex publication_date 2016/10/21 · arxiv updated 2016/10/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we propose a general framework to learn a robust large-margin binary classifier when corrupt measurements, called anomalies, caused by sensor failure might be present in the training set. The goal is to minimize the generalization error of the classifier on non-corrupted measurements while controlling the false alarm rate associated with anomalous samples. By incorporating a non-parametric regularizer based on an empirical entropy estimator, we propose a Geometric-Entropy-Minimization regularized Maximum Entropy Discrimination (GEM-MED) method to learn to classify and detect anomalies in a joint manner. We demonstrate using simulated data and a real multimodal data set. Our GEM-MED method can yield improved performance over previous robust classification methods in terms of both classification accuracy and anomaly detection rate.