基于点密集度的非线性流形学习算法  

Non-linear Manifold Learning Algorithm Based on Intensity of Points

作　　者：黄淑萍[1] 

机构地区：[1]南开大学经济学院,天津300071

出　　处：《微电子学与计算机》2012年第6期10-13,共4页Microelectronics & Computer

摘　　要：提出一种样本点密集度的非线性流形学习算法.该算法提出了一个有效的数据点密集参数,能够很好地对非均匀数据的低维嵌入进行约束,其嵌效结果明显优于LLE算法.在人工和人脸数据集上的实验结果表明,新算法产生了较好的嵌入及分类结果.This paper presents a non-linear manifold learning algorithm based on intensity of sample points. It proposes an effective intensity parameter of sample points, which constraints the low-dimensional embedding of uneven data well. There is a better embedding result than LLE. The experimental results on the artificial and face datasets show that the new algorithm yields a better embedding and classification result.

关 键 词：流形学习 组合投资 局部线性嵌入 密集度 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]