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

ISSN１００４‐９０３７，CODEN SCYCE４

Journal of Data Acquisition and Processing Vol．３２，No．６，Nov．２０１７，pp．１１４１－１１５２DOI：１０．１６３３７／j．１００４‐９０３７．２０１７．０６．００９

眗２０１７by Journal of Data Acquisition and Processing

http：／／sjcj．nuaa．edu．cn E‐mail：sjcj＠nuaa．edu．cn Tel／Fax：＋８６‐０２５‐８４８９２７４２

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

谈超１，２吉根林２赵斌２

（１．东南大学计算机科学与工程学院，南京，２１１１８９；２．南京师范大学计算机科学与技术学院，南京，２１００２３）

摘要：高维流式大数据的产生与发展对传统机器学习和数据挖掘算法提出了诸多挑战。本文结合流式大数据流式到达的特性，首先建立自适应增量特征提取算法模型。然后，针对噪声环境，建立基于特征空间校准的增量流形学习算法模型，解决小样本问题。最后，构造流形学习的正则化优化框架，解决高维数据流特征提取过程中产生的降维误差问题，并得到最终的最优解。实验结果表明本文提出的算法框架符合流形学习算法的３个评价指标：稳定性、提高性以及学习曲线能迅速增加到一个相对稳定的水平；从而实现了高维数据流的高效学习。

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

中图分类号：T P１８１文献标志码：A

Incremental Manifold Learning Regular Optimization Algorithm on Tangent Space and Feature Space Alignment

Tan Chao１，２，Ji Genlin２，Zhao Bin２

（１．School of Computer Science and Engineering，Southeast University，Nanjing，２１１１８９，China；２．School of Computer Science and Technology，Nanjing Normal University，Nanjing，２１００２３，China）

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．Key words：high dimensional big data streams；adaptive incremental feature extraction；feature space a‐lignment；regularization optimization

基金项目：国家自然科学基金（４１４７１３７１，６１７０２２７０）资助项目；江苏省高校自然科学基金（１５KJB５２００２２）资助项目；中国博士后科学基金（２０１７M６２１５９２）资助项目。

收稿日期：２０１７‐０６‐０５；修订日期：２０１７‐０６‐３０

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