Supervised Imagery Classification Based On Hierarchical Macro Manifold

被引：0

作者：

Huang, Hongbing ^{[1
]}

机构：

[1] Shanghai Jiao Tong Univ, Dept Automat, Inst Image Proc & Pattern Recognit, Shanghai 200030, Peoples R China

来源：

MECHATRONICS ENGINEERING, COMPUTING AND INFORMATION TECHNOLOGY | 2014年 / 556-562卷

关键词：

Manifold learning; dimensionality reduction; imagery classification; submanifold; generalized regression neural network;

D O I：

10.4028/www.scientific.net/AMM.556-562.4843

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Manifold learning has made many successful applications in the fields of dimensionality reduction, pattern recognition, and data visualization. In this paper we proposed hierarchical macro manifold (HMM) for the purpose of supervised classification. We construct hierarchical macro manifold based on the given training sets. The generalized regression neural network is employed to solve the out-of-sample problem. Experimental results demonstrate the feasibility and effectiveness of our proposed approach.

引用

页码：4843 / 4846

页数：4

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