电子学报2017，Vol.45Issue(6)：1362-1366,5.DOI:10.3969/j.issn.0372-2112.2017.06.012
计算和存储空间受限下的数据稀疏核分析方法
Computation and Store Space Constrained-Based Sparse Kernel Data Analysis
谢晓丹 1李伯虎 1柴旭东2
作者信息
- 1. 北京航空航天大学,北京100191
- 2. 北京仿真中心,北京 100854
- 折叠
摘要
Abstract
In order to solve the computation and storage space problems of kernel principal component analysis,which come from the large number of the training samples,this paper presents one-class support vector based sparse kernel principal component analysis (SKPCA).This method can be used in the computation-constrained and space-constrained applications,for example,a small scale hardware platform based image retrieval system,medical assistant diagnosis system,and so on.The method uses the constrained optimization equation to seek the few representative samples,and the few representative samples are used to compute the kernel matrix.The method decreases the computing time and decreases the storage space.So under conditions of the limited training samples,the method is to improve the performance of accuracy and efficiency for hardware computing platform-based image processing.关键词
主成分分析/核方法/稀疏学习Key words
principal component analysis/kernel method/sparse learning分类
信息技术与安全科学引用本文复制引用
谢晓丹,李伯虎,柴旭东..计算和存储空间受限下的数据稀疏核分析方法[J].电子学报,2017,45(6):1362-1366,5.
