摘要
针对传统子空间线性分析方法无法准确地描述样本具有的流形结构的问题,依据典型相关分析算法和局部保持投影算法的理论,将两种算法结合起来,提出了限制类别的典型相关分析的局部保持投影算法,该算法通过引入类信息,在区分了样本类信息的基础上,又保持样本类内的局部信息结构,而且还使两组样本间达到最大相关化以及各个特征投影之间具有不相关性,极大地提高了算法的识别率.该算法分别在YALE人脸库和AR人脸库上进行实验,识别率最高可达98%.
According to the problem that conventional subspace linear analysis method can＇ t describe the man- ifold structure of the sample accurately, based on the theory of canonical component analysis algorithm and locality preserving projections algorithm, we combine two kinds of algorithms. The article puts forward restricted-class lo- cality preserving canonical component analysis algorithm. The algorithm distinguishes the sample information and keeps the local information structure of the sample by introducing the class information. The algorithm achieves the level of maximum related on the base of making a distinction of the sample class information. It as well as makes the feature projections have no relativity. This algorithm significantly improves the algorithm＇ s recognition rate. The algorithm respectively does the experiment on the Yale face and AR face database. The recognition rate can reach to 98%.
出处
《哈尔滨理工大学学报》
CAS
2013年第5期65-69,共5页
Journal of Harbin University of Science and Technology
基金
黑龙江省教育厅科学技术研究项目(11551087)
关键词
典型相关分析算法
局部保持投影算法
类信息
最大相关化
canonical component analysis algorithm
locality preserving projections algorithm
class informa-tion
maximum correlation
