电子学报2016，Vol.44Issue(3)：658-664,7.DOI:10.3969/j.issn.0372-2112.2016.03.025
基于局部约束编码的稀疏保持投影降维识别方法研究
Sparsity Preserving Projections Based on Locality Constrained Coding with Applications for Targets Recognition
摘要
Abstract
Constructing graph by sparse representation ( SP) can reduce the dimensionality reduction ( DR) which re-lies on neighborhood parameter selection.However,these DR algorithms are usually unable to take sparse reconstruction into consideration while preserving local data structure.This paper presents a sparsity preserving projections based on locality-constrained coding ( LCC-SPP) algorithm.Firstly,an“adjacent” weight matrix of dataset is constructed by sparse represen-tation based classification ( SRC) .Then,a locality adaptor is introduced and the dimension reduction is modeled.We derive the solution of objective function.The similarities and differences are presented with sparse preserving projections ( SPP ) and soft locality preserving projections ( SLPP) ,respectively.At last,the recognition flow is given.We conduct experiments on databases designed for face and synthetic aperture radar ( SAR) images recognition.Considering the data locality,the pro-posed method has better recognition performance than SPP and SLPP.关键词
目标识别/维数约简/稀疏表示/局部约束编码Key words
target recognition/dimensionality reduction/sparse representation/locality constrained coding分类
信息技术与安全科学引用本文复制引用
张静,杨智勇,王国宏,林洪文,刘晓娣..基于局部约束编码的稀疏保持投影降维识别方法研究[J].电子学报,2016,44(3):658-664,7.基金项目
国家自然科学基金 （）
