中文摘要：

稀疏表示技术的引入可有效解决降维处理对图参数的依赖,但这类降维方法不能同时兼顾稀疏重构和样本数据的邻近性问题.针对该问题,本文提出了一种基于局部约束编码的稀疏保持投影降维识别方法.通过稀疏表示分类模型构建了图边权矩阵,引入局部约束因子设计了降维投影模型,推导降维求解过程,分析了本文方法与SPP（Sparse Preserving Projections）和SLPP（Soft Locality Preserving Projections）方法之间的共性和区别,最后给出了识别算法流程.采用人脸图像数据集和高分辨SAR（Synthetic Aperture Radar）图像数据集对算法的有效性进行仿真验证,由于考虑了数据间的邻近性,本文方法较传统方法可获得更好的识别性能.

英文摘要：

Constructing graph by sparse representation（ SP） can reduce the dimensionality reduction（ DR） w hich relies on neighborhood parameter selection. How ever,these DR algorithms are usually unable to take sparse reconstruction into consideration w hile preserving local data structure. This paper presents a sparsity preserving projections based on localityconstrained coding（ LCC-SPP） algorithm. Firstly,an＂adjacent＂w eight matrix of dataset is constructed by sparse representation 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 w ith 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 proposed method has better recognition performance than SPP and SLPP.