[1] 黄晓辉.高维数据的若干聚类问题及算法研究[D]. 哈尔滨: 哈尔滨工业大学, 2015.

    Huang Xiaohui. The study of clustering problem and algorithm of high dimensional data[D]. Harbin: Harbin Institute of Technology, 2015.

[2] 王卫卫, 李小平, 冯象初, 等. 稀疏子空间聚类综述[J]. 自动化学报, 2015, 41(8): 1373-1384.

    Wang W W, Li X P, Feng X C, et al. A survey on sparse subspace clustering[J]. Acta Automatica Sinica, 2015, 41(8): 1373-1384.

[3] Gumus E, Kilic N, Sertbas A, et al. Evaluation of face recognition techniques using PCA, wavelets and SVM[J]. Expert Systems with Appl., 2010, 37(9): 6404-6408.

[4] Zhou C, Wang L, Zhang Q, et al. Face recognition based on PCA image reconstruction and LDA[J]. Optik-Inter. J. for Light and Electron. Opt., 2013, 124(22): 5599-5603.

[5] Tenenbaum J B, Silva V D, Langford J C. A global geometric framework for nonlinear dimensionality reduction[J]. Science, 2000, 290(5500): 2319-2323.

[6] Wu F C, Hu Z Y. The LLE and a linear mapping[J]. Pattern Recognition, 2006, 39(9): 1799-1804.

[7] Belkin M, Niyogi P. Laplacian eigenmaps for dimensionality reduction and data representation[J]. Neural Computation, 2003, 15(6): 1373-1396.

[8] De Ridder D, Kouropteva O, Okun O, et al. Supervised locally linear embedding[C]// Proc. of Joint Inter. Conf. on Artificial Neural Networks and Neural Information, 2003: 333-341.

[9] 李白燕, 李 平, 陈庆虎. 基于改进的监督LLE人脸识别算法[J]. 电视技术, 2011, 35(19): 105-108.

    Li Baiyan, Li Ping, Chen Qinghu. Face recognition algorithm based on improved supervised LLE[J]. Video Engin., 2011, 35(19): 105-108.

[10] 薄翠梅, 韩晓春, 易 辉, 等. 基于聚类选择k近邻的LLE算法及故障检测[J]. 化工学报, 2016, 67(3): 925-930.

    Bo Cuimei, Han Xiaochun, Yi Hui, et al. Neighborhood selection of LLE based on cluster for fault detection[J]. J. of Chem. Engin., 2016, 67(3): 925-930.

[11] 夏士雄, 李佑文, 周 勇, 等. 一种半监督局部线性嵌入算法的文本分类方法[J]. 计算机应用研究, 2010, 27(1): 64-67.

    Xia Shixiong, Li Youwen, Zhou Yong, et al. Method based on semi-supervised local linear embedding algorithm for text classification[J]. Appl. Research of Computers, 2010, 27(1): 64-67.

[12] 庞彦伟, 俞能海, 沈道义, 等. 基于核邻域保持投影的人脸识别[J]. 电子学报, 2006, 34(8): 1542-1544.

    Pang Yanwei, Yu Nenghai, Shen Daoyi, et al. Kernel neighborhood preserving projections for face recognition[J]. Acta Electron. Sinica, 2006, 34(8): 1542-1544.

[13] 苏锦旗, 张文宇. 基于模糊聚类的改进LLE算法[J]. 计算机与现代化, 2014(5): 10-13.

    Su Jinqi, Zhang Wenyu. An improved LLE dimensionality reduction algorithm based on FCM[J]. Computer and Modernization, 2014(5): 10-13.

[14] 狄 晨, 王伟智. 基于改进距离的LLE算法在人脸识别中的应用[J]. 福建电脑, 2009, 25(11): 157-158.

    Di Chen, Wang Weizhi. The application in face recognition based on improved distance of the LLE algorithm[J]. Fujian Computer, 2009, 25(11): 157-158.

[15] 许晓丽. 基于聚类分析的图像分割算法研究[D]. 哈尔滨: 哈尔滨工程大学, 2012.

    Xu Xiaoli.The research of the segmentation algorithm based on clustering analysis[D]. Harbin: Harbin Engineering University, 2012.

[16] 赵小强, 谢亚萍. 基于局部线性嵌入的半监督仿射传播聚类算法[J]. 兰州理工大学学报, 2015, 41(1): 96-100.

    Zhao Xiaoqiang, Xie Yaping. Semi-supervised affinity propagation clustering algorithm based on local linear embedding[J]. J. of Lanzhou University of Technol., 2015, 41(1): 96-100.

[17] 侯小丽. 高维数据聚类中的神经网络降维方法研究[D]. 兰州: 兰州大学, 2015.

    Hou Xiaoli. The research of neural network reduction method in high dimensional data clustering[D]. Lanzhou: Lanzhou University, 2015.

[18] 文贵华, 包 丽, 丁月华. 局部线性嵌入算法中参数的选取[J]. 计算机应用研究, 2007, 24(2): 60-62.

    Wen Guihua, Bao Li, Ding Yuehua. Determining parameter for locally linear embedding algorithm[J]. Appl. Research of Computers, 2007, 24(2): 60-62.