A Non-intrusive Correction Algorithm for Classification Problems with Corrupted Data  

作　　者：Jun Hou Tong Qin Kailiang Wu Dongbin Xiu 

机构地区：[1]Department of Mathematics,The Ohio State University,Columbus,OH 43210,USA

出　　处：《Communications on Applied Mathematics and Computation》2021年第2期337-356,共20页应用数学与计算数学学报（英文）

摘　　要：A novel correction algorithm is proposed for multi-class classification problems with corrupted training data.The algorithm is non-intrusive,in the sense that it post-processes a trained classification model by adding a correction procedure to the model prediction.The correction procedure can be coupled with any approximators,such as logistic regression,neural networks of various architectures,etc.When the training dataset is sufficiently large,we theoretically prove(in the limiting case)and numerically show that the corrected models deliver correct classification results as if there is no corruption in the training data.For datasets of finite size,the corrected models produce significantly better recovery results,compared to the models without the correction algorithm.All of the theoretical findings in the paper are verified by our numerical examples.

关 键 词：Data corruption Deep neural network CROSS-ENTROPY Label corruption Robust loss 

分 类 号：TN9[电子电信—信息与通信工程]