摘要
保局投影(LPP)忽略了数据的类别标记信息且鲁棒性较差,为此,提出一种线性判别投影(LDP)算法。引入类间权重矩阵和类内权重矩阵,使各流形间的分离性最大,局部子流形的内在紧致性最小,同时通过一种鲁棒的类内处理方式使算法对outlier数据具有鲁棒性。在ORL、AR和Extended Yale B人脸数据集上进行实验,结果表明,与PCA、LDA、LPP、LSDA和LPDP算法相比,该算法的最佳平均识别率较高,分别可达95.3%、93.64%和96.28%,证明了算法的有效性和可靠性。
Because Locality Preserving Projection（LPP） ignores the label information of the data and it is lack of robustness, this paper proposes a Linear Discriminant Projection（LDP） algorithm. By introducing between-class weight matrix and within-class weight matrix, LDP maximizes the separability of different submanifolds and minimizes the compactness of local submanifolds. Moreover, LDP is robust to outlier data by a robust within-class processing way. Compared with PCA, LDA, LPP, LSDA, LPDP, the experimental results on ORL, AR and Extended Yale B face databases show that the best average recognition rates of LDP are higher, which can reach 95.3%, 93.64% and 96.28%, and this verifies the efficiency of the proposed algorithm.
出处
《计算机工程》
CAS
CSCD
2013年第11期169-173,共5页
Computer Engineering
基金
国家自然科学基金资助项目(61170109
61100119
11001247)
浙江省科技厅基金资助项目(2012C21021)
关键词
降维
流形学习
判别投影
有监督学习
保局投影
线性判别分析
dfmensionality reduction
manifold learning
discriminant projection
supervised learning
Locality Preserving Projection（LPP）
Linear Discriminant Analysis（LDA）
