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| 一种基于核的监督流形学习算法 | |
| 引用本文： | 李君宝,潘正祥.一种基于核的监督流形学习算法[J].模式识别与人工智能,2008,21(3):388-393. |
| 作者姓名： | 李君宝 潘正祥 |
| 作者单位： | 1.哈尔滨工业大学 自动化测试与控制研究所 哈尔滨 150001 2.高雄应用科技大学 电子工程系 高雄 中国 |
| 摘 要： | 针对流形学习算法——局部保持映射存在的参数选择及不能进行非线性特征提取的问题,提出一种基于核的监督流形学习算法.该算法作为局部保持映射算法的改进算法用样本类标识信息指导建立局部最近邻图,并在建立局部最近邻图使用无参数的相似度量.利用核方法来解决局部保持映射算法在处理线性不可分问题上的局限性问题.在两个常用数据库上验证本文算法的可行性和有效性. |
| 关 键 词： | 流形学习 局部保持映射(LPP) 核学习 监督学习 特征提取 |
| 收稿时间： | 2007-01-15 |
A Kernel Based Supervised Manifold Learning Algorithm |
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| LI Jun-Bao,PAN Jeng-Shyang.A Kernel Based Supervised Manifold Learning Algorithm[J].Pattern Recognition and Artificial Intelligence,2008,21(3):388-393. | |
| Authors: | LI Jun-Bao PAN Jeng-Shyang |
| Affiliation: | 1.Institute of Automatic Test and Control, Harbin Institute of Technology, Harbin 1500012. Department of Electronic Engineering, Kaohsiung University of Applied Sciences, Kaohsiung China |
| Abstract: | A kernel based supervised manifold learning method is presented to solve the problems on parameter selection with locality preserving projection and inability in nonlinear feature extraction, which is unresolved by the currently proposed manifold learning algorithm. The proposed algorithm is an improvement of locality preserving projection (LPP). The nearest neighbor graph is created with the class label information of the samples, and nonparametric similarity measure is used. The kernel method is used to solve the limitation problem of the nonlinear separability for LPP. The feasibility and effectivity of the proposed algorithm are testified on two databases. |
| Keywords: | Manifold Learning Locality Preserving Projection (LPP) Kernel Learning Supervised Learning Feature Extraction |
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