基于核融合的多信息流形学习算法*

流形学习算法可分为全局流形学习与局部流形学习,它们分别保持了流形上的全局特征信息与局部特征信息。但是实验证明仅基于单一特征信息的流形学习算法不能很好的保持真实的流形结构,影响了学习效果。因此,基于流形学习的核的视角,将全局流形学习算法ISOMAP与局部流形学习算法LTSA的核进行融合,提出了可以同时保持流形结构的全局特征信息与局部特征信息的流形学习算法,在人工数据集和人脸图像集上的仿真实验证明了本文算法的有效性。

Multi-information Manifold Learning Based On Nuclear Fusion

Manifold learning algorithms can be divided into global manifold learning and local manifold learning, and they keep global features and local features of manifolds respectively. However, experiments show that manifold learning algorithm based only on single feature information can not maintain the real structure of manifold well, affecting the learning results. Therefore, in the view of nuclear, we present a multi-information manifold learning algorithm based on the fusion of nuclear which is from the ISOMAP and LTSA. The proposed algorithm can maintain the global and local features of manifolds synchronously, and the experimental results on several synthetic data and standard face databases indicate the effectiveness of the algorithm.