[3] HUGHES G.On the mean accuracy of statistical pattern recognizers[J].IEEE Transactions on Information Theory, 2003, 14(1):55-63.
[8] WOLD S.Principal component analysis[J].Chemometrics & Intelligent Laboratory Systems, 1987, 2(1):37-52.
[9] COMON P.Independent component analysis.A new concept? [J].Signal Processing, 1994, 36(3):287-314.
[10] WANG J, CHANG C I.Mixed PCA/ICA spectral/spatial compression for hyperspectral imagery[C]//Proceedings of SPIE-The International Society for Optical Engineering, 2005.doi:10.1117/12.626613.
[11] BANDOS T V, BRUZZONE L, CAMPS-VALLS G.Classification of hyperspectral images with regularized linear discriminant analysis[J].IEEE Transactions on Geoscience & Remote Sensing, 2009, 47(3):862-873.
[13] FAUVEL M, CHANUSSOT J, BENEDIKTSSON J A.Kernel principal component analysis for the classification of hyperspectral remote sensing data over urban areas[J].EURASIP Journal on Advances in Signal Processing, 2009.doi:10.1155/2009/783194.
[14] BAI L, XU A, GUO P, et al.Kernel ICA feature extraction for spectral recognition of celestial objects[C]// IEEE International Conference on Systems, Man and Cybernetics, 2006:3922-3926.
[15] ROWEIS S, SAUL L.Nonlinear dimensionality reduction by locally linear embedding[J].Science, 2000, 290(5500):2323-2326.
[16] TENENBAUM J B, DE SILVA V, LANGFORD J C.A global geometric framework for nonlinear dimensionality reduction[J].Science, 2000, 290(5500):2319-2323.
[17] BELKIN M, NIYOGI P.Laplacian eigenmaps and spectral techniques for embedding and clustering[J].Advances in Neural Information Processing Systems, 2002, 14(6):585-591.
[18] DONOHO D L, GRIMES C.Hessian eigenmaps:locally linear embedding techniques for high-dimensional data[J].Proceedings of the National Academy of Sciences of the United States of America, 2003, 100(10):5591-5596.
[20] FAN M, QIAO H, ZHANG B, et al.Isometric multi-manifold learning for feature extraction[C]//IEEE International Conference on Data Mining, 2012:241-250.
[21] HE X F, NIYOGI P.Locality preserving projections[J].Advances In Neural Information Processing Systems, 2003, 16(1):186-197.
[22] HE X F, CAI D, YAN S C, et al.Neighborhood preserving embedding[C]//IEEE International Conference on Computer Vision, 2005:1208-1213.
[23] DE RIDDER D, KOUROPTEVA O, OKUN O, et al.Supervised locally linear embedding[C]//Artificial Neural Networks and Neural Information Processing-ICANN/ICONIP 2003, 2003:333-341.
[24] LI H, JIANG T, ZHANG K.Efficient and robust feature extraction by maximum margin criterion[J].IEEE Tran-sactions on Neural Networks, 2006, 17(1):157-165.
[25] CAI D, HE X, ZHOU K, et al.Locality sensitive discri-minant analysis[C]//The 20th International Joint Conference on Artificial Intelligence, 2007:708-713.
[26] CAI D, HE X, HAN J.Semi-supervised discriminant analysis[C]//IEEE International Conference on Computer Vision, 2007.doi:10.1109/ICCV.2007.4408856.
[27] SONG Y, NIE F, ZHANG C, et al.A unified framework for semi-supervised dimensionality reduction[J].Pattern Recognition, 2008, 41(9):2789-2799.
[29] KRIZHEVSKY A, SUTSKEVER I, HINTON G.ImageNet classification with deep convolutional neural networks[C]//NIPS, 2012.doi:10.1145/3065386.
[30] HINTON G, SALAKHUTDINOV R.Reducing the dimensionality of data with neural networks[J].Science, 2006, 313(5786):504-507.
[31] HINTON G E, OSINDERO S, TEH Y W.A fast learning algorithm for deep belief nets[J].Neural Computation, 2006, 18(7):1527-1554.
[32] LIN M, CHEN Q, YAN S.Network in network[J/OL].Computer Science, 2013.[2020-04-20].https://arxiv.org/pdf/1312.4400.pdf.
[33] SZEGEDY C, LIU W, JIA Y Q, et al.Going deeper with convolutions[C]//IEEE Conference on Computer Vision and Pattern Recognition(CVPR), 2015.doi:10.1109/CVPR.2015.7298594.
[34] SIMONYAN K, ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[J/OL].Computer Science, 2014.[2020-04-18].https://www.arxiv-vanity.com/papers/1409.1556/.
[35] WAN L, ZEILER M D, ZHANG S, et al.Regularization of neural networks using dropconnect[C]//International Conference on Machine Learning, 2013:1058-1066.