数据采集与处理2017，Vol.32Issue(6)：1141-1152,12.DOI:10.16337/j.1004-9037.2017.06.009
结合切空间及特征空间校准的增量流形学习正则优化算法
Incremental Manifold Learning Regular Optimization Algorithm on Tangent Space and Feature Space Alignment
摘要
Abstract
The emergence and development of high dimensional big data streams have presented a great challenge to the traditional machine learning and data mining algorithms .Based on the characteristics of data flow ,first we construct an adaptive incremental feature extraction algorithm model .Then ,accord-ing to the environment with noise ,we establish an incremental manifold learning algorithm model based on feature space alignment to solve the small size sample problem .Finally ,the regularization optimiza-tion framework of manifold learning is constructed to solve the problem of dimensionality reduction errors of high-dimensional data flow in feature extraction process ,and then the optimal solutions are obtained . Experimental results show that the proposed algorithm framework conforms to the three evaluation crite-rions of manifold learning algorithm :Stability ,enhancement ,and the learning curve can rapidly increase to a relative stable level .Thus the efficient learning of high-dimensional data streams can be realized .关键词
高维流式大数据/自适应增量特征提取/特征空间校准/正则化优化Key words
high dimensional big data streams/adaptive incremental feature extraction/feature space a-lignment/regularization optimization分类
信息技术与安全科学引用本文复制引用
谈超,吉根林,赵斌..结合切空间及特征空间校准的增量流形学习正则优化算法[J].数据采集与处理,2017,32(6):1141-1152,12.基金项目
国家自然科学基金(41471371,61702270)资助项目 （41471371,61702270）
江苏省高校自然科学基金(15KJB520022)资助项目 （15KJB520022）
中国博士后科学基金(2017M621592)资助项目. （2017M621592）
