数据采集与处理2018，Vol.33Issue(3)：504-511,8.DOI:10.16337/j.1004-9037.2018.03.014
适用于小样本的双邻接图判别分析算法
Double Adjacent Graph-Based Discriminant Analysis for Small Size Sample
摘要
Abstract
As a common dimensionality reduction method ,the supervised Laplacian discriminant analysis (SLDA) for small size sample achieves a good result of dimensionality reduction via graph embedding dis-criminant neighborhood analysis .However ,when SLDA finds the inter-class and intra-class data points in K nearest neighbors ,there might exist an imbalance problem .Additionally ,SLDA does not fully con-sider the inter-class information ,which may decrease the performance of SLDA to a certain extent .To address the two problems mentioned above ,we propose a double adjacent graph-based discriminant anal-ysis (DAG-DA) algorithm for small size sample .Firstly ,the algorithm tries to find K nearest neighbors in inter-class and intra-class samples ,respectively ,and then uses these K inter-class neighbors and K in-tra-class neighbors to construct the double adjacent graph .In this way ,we can ensure that the adjacent graph contains both the inter-class and intra-class data points and has the same number .Secondly ,the al-gorithm tries to add the intra-class Laplacian scatter matrix into the objective function of SLDA .Thus ,the projection matrix obtained by optimization takes the information between classes into account fully . We perform experiments on Yale and ORL human face datasets .Experimental results show that the pro-posed algorithm can get better performance compared with other methods .关键词
人脸识别/拉普拉斯判别分析/双邻接图/降维Key words
face recognition/Laplacian discriminant analysis/double adjacency graph/dimensionality re-duction分类
信息技术与安全科学引用本文复制引用
罗璇,张莉,薛杨涛,李凡长..适用于小样本的双邻接图判别分析算法[J].数据采集与处理,2018,33(3):504-511,8.基金项目
国家自然科学基金(61373093 ,61402310)资助项目 （61373093 ,61402310）
江苏省自然科学基金(BK20140008 ,BK2012624) 资助项目 （BK20140008 ,BK2012624）
江苏省高校自然科学研究 (13KJA520001)资助项目. （13KJA520001）
