全局加权稀疏局部保留投影  

Global weighted sparse locality preserving projection

作　　者：林克正[1] 程卫月[1] 刘帅[1] 

机构地区：[1]哈尔滨理工大学计算机科学与技术学院,哈尔滨150080

出　　处：《计算机应用》2014年第3期760-762,779,共4页journal of Computer Applications

基　　金：国家自然科学基金资助项目(60873019);黑龙江省教育厅科学技术研究项目(11551087)

摘　　要：针对稀疏保留投影(SPP)算法运行时间较长并且忽略了样本的类间差异信息的问题,在稀疏保留投影算法的基础上,提出了全局加权稀疏局部保留投影(GWSLPP)算法。该算法在保持样本的稀疏重构关系的同时,使样本具有很好的鉴别能力,算法通过对样本进行稀疏重构处理;然后对样本进行投影并且最大化样本的类间散度;最后利用得到的投影将样本分类。该算法分别在FERET人脸库和YALE人脸库上进行实验。实验结果表明,全局加权稀疏保留算法在执行时间和识别率综合性能上,优于局部保留投影(LPP)、SPP和FisherFace算法,执行时间只有25 s,识别率能达到95%以上,实验数据验证了算法的有效性。For the problems of long runtime, ignoring the difference between classes of sample, the paper put forward an algorithm called Global Weighted Sparse Locality Preserving Projection （GWSLPP） based on Sparse Preserving Projection （ SPP）. The algorithm made sample have good identification ability while maintaining the sparse reconstruction relations of the samples. The algorithm processed the samples though sparse reconstruction, then made the sample on the projection and maximized the divergence between classes of sample. It got the projection and classified the sample at last. The algorithm made the experiments on FERET face database and YALE face database. The experimental results show the GWSLPP algorithm is superior to the Locality Preserving Projection （LPP）, SPP and FisherFace algorithm in both execution time and recognition rate. The execution time is only 25 s and the recognition rate can reach more than 95%. The experimental data prove the effectiveness of the algorithm.

关 键 词：稀疏保留投影算法 类间差异 稀疏重构 类间散度 

分 类 号：TP391.413[自动化与计算机技术—计算机应用技术] TP18[自动化与计算机技术—计算机科学与技术]