Semi-supervised Dictionary Active Learning for Pattern Classification
Gathering labeled data is one of the most time-consuming and expensive tasks in supervised machine learning.In practical applications,there are usually quite limited labeled training samples but abundant unlabeled data that is easy to collect.Semi-supervised learning and active learning are two important techniques for learning a discriminative classification model when labeled data is scarce.However,unlabeled data with significant noises and outliers cannot be well exploited and usually worsen the performance of semisupervised learning and the performance of active learning also needs a powerful initial classifier learned from the quite limited labeled training data.In order to solve the above issues,in this paper we proposed a novel model of semi-supervised dictionary active learning(SSDAL),which aims to integrate semi-supervised learning and active learning to effectively use all the training data.In particular,two criterions based on estimated class possibility are designed to select the unlabeled data with confident class estimation for semisupervised learning and the informative unlabeled data for active learning,respectively.Extensive experiments are conducted to show the superior performance of our method in classification applications,e.g.,handwritten digit recognition,face recognition and large-scale image classification.
Semi-supervised learning Dictionary learning Active learning Pattern classification
Qin Zhong Meng Yang Tiancheng Zhang
School of Data and Computer Science,Sun Yat-sen University,Guangzhou,China;School of Computer Scienc School of Data and Computer Science,Sun Yat-sen University,Guangzhou,China School of Computer Science and Engineering,Northeastern University,Shenyang,China
国际会议
广州
英文
560-572
2018-11-23（万方平台首次上网日期，不代表论文的发表时间）