自动化学报2016，Vol.42Issue(9)：1313-1321,9.DOI:10.16383/j.aas.2016.c150560
基于边际Fisher准则和迁移学习的小样本集分类器设计算法
Classifier-designing Algorithm on a Small Dataset Based on Margin Fisher Criterion and Transfer Learning
摘要
Abstract
It has great practical significance to design a classifier on a small dataset (target domain) with the help of a large dataset (source domain). Since feature distribution varies on different datasets, the classifiers trained on the source domain cannot perform well on a target domain. To solve the problem, we propose a novel classifier-designing algorithm based on transfer learning theory. Firstly, to improve the compass of the same category and separateness of different categories in the source domain, this paper utilizes the extended margin Fisher criterion where the distance is measured by the inner product between data. Secondly, to select good sample pairs for transfer learning, this paper presents an algorithm to get rid of marginal singular points by selecting high-density samples in the source domain. The non-linear feature transformation mapping the target domain to the source domain is learned in the kernel space. Finally, k-nearest neighbor (kNN) classifiers are trained for classification. Compared with the existing works, this paper not only extends the margin Fisher criterion, but also applies the transfer learning theory based on the algorithm of selecting training sample pairs to design classifiers of a small dataset. We experimentally demonstrate the superiority of our method to effectively improve the performance of classifiers on the general datasets.关键词
小样本集分类器/迁移学习/边际Fisher准则/kNN分类器/域间转换Key words
Classifiers on a small dataset/transfer learning/margin Fisher criterion/k-nearest neighbor (kNN) classifiers/domain transformation引用本文复制引用
舒醒,于慧敏,郑伟伟,谢奕,胡浩基,唐慧明..基于边际Fisher准则和迁移学习的小样本集分类器设计算法[J].自动化学报,2016,42(9):1313-1321,9.基金项目
国家自然科学基金(61471321),教育部-中国移动科研基金(MCM20150503),国家自然科学基金(61202400),浙江省自然科学基金(LQ12F02014)资助Supported by National Natural Science Foundation of China (61471321), Ministry of Education - China Mobile Research Fund (MCM20150503), National Natural Science Foundation of China (61202400), and Natural Science Foundation of Zhejiang Province (LQ12F02014) （61471321）
