[1] ABDI H, WILLIAMS L J. Principal component analysis[J]. Wiley Interdisciplinary Reviews Computational Statistics, 2010, 2(4):433-459. [2] AN L L, XING H J. Linear discriminant analysis based on Zp-norm maximization[C]//Proceedings of the 20142nd International Conference on Information Technology and Electronic Commerce. Piscataway, NJ:IEEE, 2014:88-92. [3] 甘炎灵,金聪. SRC最佳鉴别投影及其在人脸识别中的应用[J]. 计算机工程与科学, 2016, 38(11):139-145. (GAN Y L, JIN C. SRC optimal discriminate projection and its application in face recognition[J]. Computer Engineering and Science, 2016, 38(11):139-145.) [4] YAN H, LU J, ZHOU X, et al. Multi-feature multi-manifold learning for single-sample face recognition[J]. Neurocomputing, 2014, 143(16):134-143. [5] DE SILVA V, TENENBAUM J B. Global versus local methods in nonlinear dimensionality reduction[J]. Advances in Neural Information Processing Systems, 2003, 15:1959-1966. [6] CHANG H, YEUNG D Y. Robust locally linear embedding[J]. Pattern Recognition, 2006, 39(6):1053-1065. [7] FANG Y, LI H, MA Y, et al. Dimensionality reduction of hyperspectral images based on robust spatial information using locally linear embedding[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(10):1712-1716. [8] ZHANG L, QIAO L, CHEN S. Graph-optimized locality preserving projections[J]. Pattern Recognition, 2010, 43(6):1993-2002. [9] MARTINEZ-DEL-RINCON J, LEWANDOWSKI M, NEBEL J C, et al. Generalized Laplacian eigenmaps for modeling and tracking human motions[J]. IEEE Transactions on Cybernetics, 2014, 44(9):1646-1660. [10] KIM K, LEE D. Inductive manifold learning using structured support vector machine[J]. Pattern Recognition, 2014, 47(1):470-479. [11] WANG J. Hessian locally linear embedding[M]//Geometric Structure of High-Dimensional Data and Dimensionality Reduction. Berlin:Springer, 2012:249-265. [12] QIU X, WU L. Face recognition by stepwise nonparametric margin maximum criterion[C]//Proceedings of the 10th IEEE International Conference on Computer Vision. Piscataway, NJ:IEEE, 2005:1567-1572. [13] HUANG P, TANG Z, CHEN C, et al. Local maximal margin discriminant embedding for face recognition[J]. Journal of Visual Communication and Image Representation, 2013, 25(2):296-305. [14] YAN S, XU D, ZHANG B, et al. Graph embedding:a general framework for dimensionality reduction[C]//Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Piscataway, NJ:IEEE, 2005:830-837. [15] YAN S, XU D, ZHANG B, et al. Graph embedding and extensions:a general framework for dimensionality reduction[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(1):40-51. [16] LI H, JIANG T, ZHANG K. Efficient and robust feature extraction by maximum margin criterion[J]. IEEE Transactions on Neural Networks, 2006, 17(1):157-165. [17] DE CARVALHO F A T, TENÓRIO C P. Fuzzy K-means clustering algorithms for interval-valued data based on adaptive quadratic distances[J]. Fuzzy Sets and Systems, 2010, 161(23):2978-2999. [18] ZHU X, GHAHRAMANI Z, LAFFERTY J. Semi-supervised learning using Gaussian fields and harmonic functions[C]//Proceedings of the 20th International Conference on Machine Learning. Washington, DC:AAAI Press, 2003, 3:912-919. [19] WANG S J, YAN S, YANG J, et al. A general exponential framework for dimensionality reduction[J]. IEEE Transactions on Image Processing, 2014, 23(2):920-930. [20] 张贤达. 矩阵分析与应用[M]. 2版.北京:清华大学出版社, 2004:229-243.(ZHANG X D. Matrix Analysis and Applications[M]. 2nd ed. Beijing:Tsinghua University Press, 2004:229-243.) [21] GOLUB G H, VAN LOAN C F. Matrix Computations[M]. Baltimore:JHU Press, 2013:458-466. This work was supported by the National Social Science Foundation of China (13BTQ050).GAN Yanling, born in 1988, M.S.candidate. Her research interests include dimensionality reduction, pattern recognition.JIN Cong, born in 1960, Ph. D., professor. Her research interests include image processing, intelligent information processing. |