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

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。

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

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, according 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 optimization 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 criterions 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.